Paper Digest: IJCAI 2024 Papers & Highlights
International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
Note: IJCAI-2024 accepts more than 1,000 papers, this page only includes 500 of them selected by our daily paper digest ranking algorithm. To browse all accepted papers or learn more about the IJCAI-2024 statistics, readers can read All IJCAI-2024 accepted papers in a separate page, which takes quite some time to load. On this pape, readers are also able to filter papers by keywords. For example, using ‘related code’ as the filter keyword will produce a list of all papers with code available to download.
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TABLE 1: Paper Digest: IJCAI 2024 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Learning in CubeRes Model Space for Anomaly Detection in 3D GPR Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we introduce learning in the Cube Reservoir Network (CubeRes) model space for efficient and accurate subsurface anomaly detection. |
Xiren Zhou; Shikang Liu; Ao Chen; Huanhuan Chen; |
| 2 | Towards Proactive Interactions for In-Vehicle Conversational Assistants Utilizing Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: According to the framework, we propose a “Rewrite + ReAct + Reflect” strategy, aiming to empower LLMs to fulfill the specific demands of each proactivity level when interacting with users. |
Huifang Du; Xuejing Feng; Jun Ma; Meng Wang; Shiyu Tao; Yijie Zhong; Yuan-Fang Li; Haofen Wang; |
| 3 | Large Language Model Based Multi-agents: A Survey of Progress and Challenges Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, LLM-based agent systems have rapidly evolved from single-agent planning or decision-making to operating as multi-agent systems, enhancing their ability in complex problem-solving and world simulation. To offer an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects and challenges of LLM-based multi-agent (LLM-MA) systems. |
Taicheng Guo; Xiuying Chen; Yaqi Wang; Ruidi Chang; Shichao Pei; Nitesh V. Chawla; Olaf Wiest; Xiangliang Zhang; |
| 4 | Inferring Iterated Function Systems Approximately from Fractal Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As an important mathematical concept, fractals commonly appear in nature and inspire the design of many artistic works.Although we can generate various fractal images easily based on different iterated function systems (IFSs), inferring an IFS from a given fractal image is still a challenging inverse problem for both scientific research and artistic design. In this study, we explore the potential of deep learning techniques for this problem, learning a multi-head auto-encoding model to infer typical IFSs (including Julia set and L-system) from fractal images. |
Haotian Liu; Dixin Luo; Hongteng Xu; |
| 5 | AutoAgents: A Framework for Automatic Agent Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. |
Guangyao Chen; Siwei Dong; Yu Shu; Ge Zhang; Jaward Sesay; Börje Karlsson; Jie Fu; Yemin Shi; |
| 6 | ScreenAI: A Vision-Language Model for UI and Infographics Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. |
Gilles Baechler; Srinivas Sunkara; Maria Wang; Fedir Zubach; Hassan Mansoor; Vincent Etter; Victor Carbune; Jason Lin; Jindong Chen; Abhanshu Sharma; |
| 7 | Continual Learning with Pre-Trained Models: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a comprehensive survey of the latest advancements in PTM-based CL. |
Da-Wei Zhou; Hai-Long Sun; Jingyi Ning; Han-Jia Ye; De-Chuan Zhan; |
| 8 | X-former Elucidator: Reviving Efficient Attention for Long Context Language Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we answer this question from several aspects.First, we revisit these latest long-context LLM innovations and discuss their relationship with prior approaches with a novel and comprehensive taxonomy.Next, we conduct a thorough evaluation over various types of workloads considering both efficiency and effectiveness.Finally, we provide an in-depth analysis, summarize our key findings, and offer insightful suggestions on the trade-offs of designing and deploying efficient attention mechanisms for Transformer-based LLMs. |
Xupeng Miao; Shenhan Zhu; Fangcheng Fu; Ziyu Guo; Zhi Yang; Yaofeng Tu; Zhihao Jia; Bin Cui; |
| 9 | InstructEdit: Instruction-Based Knowledge Editing for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we develop an instruction-based editing technique, termed InstructEdit, which facilitates the editor’s adaptation to various task performances simultaneously using simple instructions. |
Ningyu Zhang; Bozhong Tian; Siyuan Cheng; Xiaozhuan Liang; Yi Hu; Kouying Xue; Yanjie Gou; Xi Chen; Huajun Chen; |
| 10 | Unsupervised Deep Graph Structure and Embedding Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Label supervision is expensive in real-world applications, and thus unsupervised GSL is more challenging and still remains less studied. To fulfill this gap, this paper proposes a new unsupervised GSL method, i.e., unsupervised property GNN (UPGNN). |
Xiaobo Shen; Lei Shi; Xiuwen Gong; Shirui Pan; |
| 11 | Generating More Audios for End-to-End Spoken Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the scenarios where the transcripts are not available and propose a framework GMA-SLU to generate more audios according to the labels. |
Xuxin Cheng; Yuexian Zou; |
| 12 | Scalable Federated Unlearning Via Isolated and Coded Sharding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. |
Yijing Lin; Zhipeng Gao; Hongyang Du; Dusit Niyato; Gui Gui; Shuguang Cui; Jinke Ren; |
| 13 | Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we propose ACSleuth, a novel, reconstruction deviation-guided generative framework that integrates the detection, domain adaptation, and fine-grained annotating of anomalous cells into a methodologically cohesive workflow. |
Kaichen Xu; Yueyang Ding; Suyang Hou; Weiqiang Zhan; Nisang Chen; Jun Wang; Xiaobo Sun; |
| 14 | All in One: Multi-task Prompting for Graph Neural Networks (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper introduces a novel approach to bridging the gap between pre-trained graph models and the diverse tasks they’re applied to, inspired by the success of prompt learning in NLP. |
Xiangguo Sun; Hong Cheng; Jia Li; Bo Liu; Jihong Guan; |
| 15 | Hybrid Frequency Modulation Network for Image Restoration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an effective and efficient framework for image restoration, termed CSNet, based on “channel + spatial" hybrid frequency modulation. |
Yuning Cui; Mingyu Liu; Wenqi Ren; Alois Knoll; |
| 16 | Counterfactual User Sequence Synthesis Augmented with Continuous Time Dynamic Preference Modeling for Sequential POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Another challenge is the inherent sparsity and noise in continuous POI recommendation, which hinder the recommendation process. To address these challenges, we propose counterfactual user sequence synthesis with continuous time dynamic preference modeling (CussCtpm). |
Lianyong Qi; Yuwen Liu; Weiming Liu; Shichao Pei; Xiaolong Xu; Xuyun Zhang; Yingjie Wang; Wanchun Dou; |
| 17 | D3ETR: Decoder Distillation for Detection Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose MixMatcher that aligns the de- coder outputs of DETR-based teacher and student, by mixing two teacher-student matching strategies for combined advantages. |
Xiaokang Chen; Jiahui Chen; Yan Liu; Jiaxiang Tang; Gang Zeng; |
| 18 | Fair and Efficient Chore Allocation: Existence and Computation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the existence and computation of fair and efficient allocations of indivisible chores to agents with additive preferences. |
Aniket Murhekar; |
| 19 | Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, existing Deepfake detection methods struggle to detect unknown domain Deepfake videos while accurately locating the tampered region. To address this limitation, we propose Delocate, a novel Deepfake detection model that can both recognize and localize unknown domain Deepfake videos. |
Juan Hu; Xin Liao; Difei Gao; Satoshi Tsutsui; Qian Wang; Zheng Qin; Mike Zheng Shou; |
| 20 | Updates on The Complexity of SHAP Scores Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: SHAP scores represent one of the most widely used methods of explainability by feature attribution, as illustrated by the explainable AI tool SHAP.A number of recent works studied … |
Xuanxiang Huang; Joao Marques-Silva; |
| 21 | Safety of Multimodal Large Language Models on Images and Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we systematically survey current efforts on the evaluation, attack, and defense of MLLMs’ safety on images and text. |
Xin Liu; Yichen Zhu; Yunshi Lan; Chao Yang; Yu Qiao; |
| 22 | Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We release the codebase to reproduce the results presented in this paper at https://github.com/nyunAI/Faster-LLM-Survey |
Arnav Chavan; Raghav Magazine; Shubham Kushwaha; Merouane Debbah; Deepak Gupta; |
| 23 | MetaISP: Efficient RAW-to-sRGB Mappings with Merely 1M Parameters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: State-of-the-art deep ISP models alleviate the dilemma of limited generalization capabilities across heterogeneous inputs by increasing the size and complexity of the network, which inevitably leads to considerable growth in parameter counts and FLOPs. To address this challenge, this paper presents MetaISP – a streamlined model that achieves superior reconstruction quality by adaptively modulating its parameters and architecture in response to diverse inputs. |
Zigeng Chen; Chaowei Liu; Yuan Yuan; Michael Bi Mi; Xinchao Wang; |
| 24 | A Consistency and Integration Model with Adaptive Thresholds for Weakly Supervised Object Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Consistency and Integration Model with Adaptive Thresholds (CIAT) that exploits the spatial-semantic consistency between shallow and deep features to activate more object regions and detects the object regions adaptively in different images. |
Hao Su; Meng Yang; |
| 25 | BeyondVision: An EMG-driven Micro Hand Gesture Recognition Based on Dynamic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, vision-based methods are limited in recognizing micro hand gestures (MHGs) (e.g., pinch within 1cm) and gestures with occluded fingers. To address these issues, combined with the electromyography (EMG) technique, we propose BeyondVision, an EMG-driven MHG recognition system based on deep learning. |
Nana Wang; Jianwei Niu; Xuefeng Liu; Dongqin Yu; Guogang Zhu; Xinghao Wu; Mingliang Xu; Hao Su; |
| 26 | Fine-tuning Pre-trained Models for Robustness Under Noisy Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we aim to find an effective approach to fine-tune pre-trained models for noisy labeled datasets. |
Sumyeong Ahn; Sihyeon Kim; Jongwoo Ko; Se-Young Yun; |
| 27 | Truth Table Net: Scalable, Compact & Verifiable Neural Networks with A Dual Convolutional Small Boolean Circuit Networks Form Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce "Truth Table net"’ (TTnet), a novel Deep Neural Network (DNN) architecture designed to provide excellent scalability/compactness trade-offs among DNNs, allowing in turn to tackle the DNN challenge of fast formal verification. |
Adrien Benamira; Thomas Peyrin; Trevor Yap; Tristan Guérand; Bryan Hooi; |
| 28 | A Survey of Data-Efficient Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel concept of Data-Efficient Graph Learning (DEGL) as a research frontier, and present the first survey that summarizes the current progress of DEGL. |
Wei Ju; Siyu Yi; Yifan Wang; Qingqing Long; Junyu Luo; Zhiping Xiao; Ming Zhang; |
| 29 | LeRet: Language-Empowered Retentive Network for Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose LeRet, a Language-empowered Retentive network for TSF. |
Qihe Huang; Zhengyang Zhou; Kuo Yang; Gengyu Lin; Zhongchao Yi; Yang Wang; |
| 30 | TaD: A Plug-and-Play Task-Aware Decoding Method to Better Adapt LLMs on Downstream Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces Task-aware Decoding (TaD), a plug-and-play method that exploits the difference in probability distributions before and after fine-tuning to boost the performance of LLMs on downstream tasks. |
Xinhao Xu; Hui Chen; Zijia Lin; Jungong Han; Lixing Gong; Guoxin Wang; Yongjun Bao; Guiguang Ding; |
| 31 | A Survey on Neural Question Generation: Methods, Applications, and Prospects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images. |
Shasha Guo; Lizi Liao; Cuiping Li; Tat-Seng Chua; |
| 32 | Accelerating Diffusion Models for Inverse Problems Through Shortcut Sampling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Shortcut Sampling for Diffusion(SSD), a novel approach for solving inverse problems in a zero-shot manner. |
Gongye Liu; Haoze Sun; Jiayi Li; Fei Yin; Yujiu Yang; |
| 33 | CVAT-BWV: A Web-Based Video Annotation Platform for Police Body-Worn Video Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an open-source platform for annotating body-worn video (BWV) footage aimed at enhancing transparency and accountability in policing. |
Parsa Hejabi; Akshay Kiran Padte; Preni Golazizian; Rajat Hebbar; Jackson Trager; Georgios Chochlakis; Aditya Kommineni; Ellie Graeden; Shrikanth Narayanan; Benjamin A.T. Graham; Morteza Dehghani; |
| 34 | Manipulating Embeddings of Stable Diffusion Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on treating the model as a continuous function and by passing gradients between the image space and the prompt embedding space, we propose and analyze a new method to directly manipulate the embedding of a prompt instead of the prompt text. |
Niklas Deckers; Julia Peters; Martin Potthast; |
| 35 | Off-Agent Trust Region Policy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our approach introduces off-agent adaptations to the multi-agent policy optimization methods, enabling effective and purposeful leverage of cross-agent experiences beyond conventional parameter sharing. |
Ruiqing Chen; Xiaoyuan Zhang; Yali Du; Yifan Zhong; Zheng Tian; Fanglei Sun; Yaodong Yang; |
| 36 | Retrieval Guided Music Captioning Via Multimodal Prefixes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we put forward a new approach to music captioning, the task of automatically generating natural language descriptions for songs. |
Nikita Srivatsan; Ke Chen; Shlomo Dubnov; Taylor Berg-Kirkpatrick; |
| 37 | Regression Residual Reasoning with Pseudo-labeled Contrastive Learning for Uncovering Multiple Complex Compositional Relations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study reveals that AVR models tend to rely on appearance matching rather than a genuine understanding of underlying rules. |
Chengtai Li; Yuting He; Jianfeng Ren; Ruibin Bai; Yitian Zhao; Heng Yu; Xudong Jiang; |
| 38 | A Context-Enhanced Framework for Sequential Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we generalize the existing architectures and propose a context-enhanced framework. |
Shuo Shi; Chao Peng; Chenyang Xu; Zhengfeng Yang; |
| 39 | ScreenAgent: A Vision Language Model-driven Computer Control Agent Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we construct an environment for a Vision Language Model (VLM) agent to interact with a real computer screen. |
Runliang Niu; Jindong Li; Shiqi Wang; Yali Fu; Xiyu Hu; Xueyuan Leng; He Kong; Yi Chang; Qi Wang; |
| 40 | Understanding Public Perception Towards Weather Disasters Through The Lens of Metaphor Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we analyze public perception towards weather disasters based on tweets and metaphors. |
Rui Mao; Qika Lin; Qiawen Liu; Gianmarco Mengaldo; Erik Cambria; |
| 41 | DifTraj: Diffusion Inspired By Intrinsic Intention and Extrinsic Interaction for Multi-Modal Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we analyze the conditions of multi-modal trajectory prediction from two objective perspectives and propose a novel end-to-end framework based on the diffusion model to predict more precise and socially-acceptable trajectories for humans. |
Yanghong Liu; Xingping Dong; Yutian Lin; Mang Ye; |
| 42 | Joint Input and Output Coordination for Class-Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although storing a small amount of data on old tasks during incremental learning is a feasible solution, current strategies still do not 1) adequately address the class bias problem, and 2) alleviate the mutual interference between new and old tasks, and 3) consider the problem of class bias within tasks. In light of the above issues, we analyze the cause of class bias in incremental learning, as well as the drawbacks of existing approaches, and propose a joint input and output coordination (JIOC) mechanism to address these issues. |
Shuai Wang; Yibing Zhan; Yong Luo; Han Hu; Wei Yu; Yonggang Wen; Dacheng Tao; |
| 43 | Bring Metric Functions Into Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a Cascaded Diffusion Model (Cas-DM) that improves a Denoising Diffusion Probabilistic Model (DDPM) by effectively incorporating additional metric functions in training. |
Jie An; Zhengyuan Yang; Jianfeng Wang; Linjie Li; Zicheng Liu; Lijuan Wang; Jiebo Luo; |
| 44 | Emergence of Social Norms in Generative Agent Societies: Principles and Architecture Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel architecture, named CRSEC, to empower the emergence of social norms within generative MASs. |
Siyue Ren; Zhiyao Cui; Ruiqi Song; Zhen Wang; Shuyue Hu; |
| 45 | FactCHD: Benchmarking Fact-Conflicting Hallucination Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The accurate identification of hallucinations in texts generated by LLMs, especially in complex inferential scenarios, is a relatively unexplored area. To address this gap, we present FactCHD, a dedicated benchmark designed for the detection of fact-conflicting hallucinations from LLMs. |
Xiang Chen; Duanzheng Song; Honghao Gui; Chenxi Wang; Ningyu Zhang; Yong Jiang; Fei Huang; Chengfei Lyu; Dan Zhang; Huajun Chen; |
| 46 | Continual Multimodal Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study introduces benchmarks aimed at fostering the development of the continual MKGC domain. |
Xiang Chen; Jingtian Zhang; Xiaohan Wang; Ningyu Zhang; Tongtong Wu; Yuxiang Wang; Yongheng Wang; Huajun Chen; |
| 47 | Estimating Before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, as the models themselves are biased, merely constraining their consistency is not sufficient to alleviate prediction bias. In this paper, we explore this bias from a Bayesian perspective and demonstrate that it principally originates from label prior bias within the training data. |
Guogang Zhu; Xuefeng Liu; Xinghao Wu; Shaojie Tang; Chao Tang; Jianwei Niu; Hao Su; |
| 48 | Machine Unlearning Via Null Space Calibration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Consequently, existing unlearning algorithms degrade the model’s performance after unlearning, known as over-unlearning. This paper addresses this critical yet under-explored issue by introducing machine Unlearning via Null Space Calibration (UNSC), which can accurately unlearn target samples without over-unlearning. |
Huiqiang Chen; Tianqing Zhu; Xin Yu; Wanlei Zhou; |
| 49 | Knowledge Distillation in Federated Learning: A Practical Guide Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we originally present a focused review of the state-of-the-art KD-based algorithms specifically tailored for FL, by providing both a novel classification of the existing approaches and a detailed technical description of their pros, cons, and tradeoffs. |
Alessio Mora; Irene Tenison; Paolo Bellavista; Irina Rish; |
| 50 | RoboFusion: Towards Robust Multi-Modal 3D Object Detection Via SAM Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With the emergence of visual foundation models (VFMs), opportunities and challenges are presented for improving the robustness and generalization of multi-modal 3D object detection in AD. Therefore, we propose RoboFusion, a robust framework that leverages VFMs like SAM to tackle out-of-distribution (OOD) noise scenarios. |
Ziying Song; Guoxing Zhang; Lin Liu; Lei Yang; Shaoqing Xu; Caiyan Jia; Feiyang Jia; Li Wang; |
| 51 | Guidance Graph Optimization for Lifelong Multi-Agent Path Finding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually designed ones. In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimizing its edge weights. |
Yulun Zhang; He Jiang; Varun Bhatt; Stefanos Nikolaidis; Jiaoyang Li; |
| 52 | FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their impressive generative performance, latent diffusion model-based virtual try-on (VTON) methods lack faithfulness to crucial details of the clothes, such as style, pattern, and text. To alleviate these issues caused by the diffusion stochastic nature and latent supervision, we propose a novel Faithful Latent Diffusion Model for VTON, termed FLDM-VTON. |
Chenhui Wang; Tao Chen; Zhihao Chen; Zhizhong Huang; Taoran Jiang; Qi Wang; Hongming Shan; |
| 53 | KTCN: Enhancing Open-World Object Detection with Knowledge Transfer and Class-Awareness Neutralization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we leverage the knowledge of the large-scale visual model to provide supervision for unknown categories. |
Xing Xi; Yangyang Huang; Jinhao Lin; Ronghua Luo; |
| 54 | Reinforcement Learning from Diverse Human Preferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods for preference-based RL are limited by the need for accurate oracle preference labels. This paper addresses this limitation by developing a method for learning from diverse human preferences. |
Wanqi Xue; Bo An; Shuicheng Yan; Zhongwen Xu; |
| 55 | Learning to Solve Geometry Problems Via Simulating Human Dual-Reasoning Process Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, inspired by dual-process theory, we propose a Dual-Reasoning Geometry Solver (DualGeoSolver) to simulate the dual-reasoning process of humans for GPS. |
Tong Xiao; Jiayu Liu; Zhenya Huang; Jinze Wu; Jing Sha; Shijin Wang; Enhong Chen; |
| 56 | On The Essence and Prospect: An Investigation of Alignment Approaches for Big Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey paper, we comprehensively investigate value alignment approaches. |
Xinpeng Wang; Shitong Duan; Xiaoyuan Yi; Jing Yao; Shanlin Zhou; Zhihua Wei; Peng Zhang; Dongkuan Xu; Maosong Sun; Xing Xie; |
| 57 | Randomized Learning-Augmented Auctions with Revenue Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the fundamental problem of designing a truthful single-item auction with the challenging objective of extracting a large fraction of the highest agent valuation as revenue. |
Ioannis Caragiannis; Georgios Kalantzis; |
| 58 | DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this approach faces three challenges: invalidated when private prompt is guessed, crafting public prompts for semantic diversity, and the risk of prompt leakage during frequent transmission. To address these issues, we propose DiffStega, an innovative training-free diffusion-based CIS strategy for universal application. |
Yiwei Yang; Zheyuan Liu; Jun Jia; Zhongpai Gao; Yunhao Li; Wei Sun; Xiaohong Liu; Guangtao Zhai; |
| 59 | A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we conduct a systematic review of existing cross-domain policy transfer methods. |
Haoyi Niu; Jianming Hu; Guyue Zhou; Xianyuan Zhan; |
| 60 | Mahjong AI Competition: Exploring AI Application in Complex Real-World Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents three Mahjong AI competitions we held at IJCAI. |
Yunlong Lu; Wenxin Li; |
| 61 | A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. |
Haicheng Liao; Zhenning Li; Chengyue Wang; Bonan Wang; Hanlin Kong; Yanchen Guan; Guofa Li; Zhiyong Cui; |
| 62 | Cross-Talk Reduction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although each close-talk mixture has a high signal-to-noise ratio (SNR) of the wearer, it has a very limited range of applications, as it also contains significant cross-talk speech by other speakers and is not clean enough. In this context, we propose a novel task named \textit{cross-talk reduction} (CTR) which aims at reducing cross-talk speech, and a novel solution named CTRnet which is based on unsupervised or weakly-supervised neural speech separation. |
Zhong-Qiu Wang; Anurag Kumar; Shinji Watanabe; |
| 63 | X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer As Meta Multi-Agent Reinforcement Learner Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, a persisting issue remains: how to obtain a multi-agent traffic signal control algorithm with remarkable transferability across diverse cities? In this paper, we propose a Transformer on Transformer (TonT) model for cross-city meta multi-agent traffic signal control, named as X-Light: We input the full Markov Decision Process trajectories, and the Lower Transformer aggregates the states, actions, rewards among the target intersection and its neighbors within a city, and the Upper Transformer learns the general decision trajectories across different cities. |
Haoyuan Jiang; Ziyue Li; Hua Wei; Xuantang Xiong; Jingqing Ruan; Jiaming Lu; Hangyu Mao; Rui Zhao; |
| 64 | Decoupling Breaks Data Barriers: A Decoupled Pre-training Framework for Multi-intent Spoken Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, current multi-intent SLU approaches still heavily rely on large amounts of annotated multi-intent SLU data, which makes it hard to be satisfied in real-world scenarios without sufficient data. Motivated by this, we introduce a novel decoupled pre-training framework (DPF) to address the data-scarcity problem, achieving to leverage of abundant multi-intent-free SLU data to enhance multi-intent SLU. |
Libo Qin; Qiguang Chen; Jingxuan Zhou; Qinzheng Li; Chunlin Lu; Wanxiang Che; |
| 65 | Natural Language Decomposition and Interpretation of Complex Utterances Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an approach to handle complex-intent-bearing utterances from a user via a process of hierarchical natural language decomposition and interpretation. |
Harsh Jhamtani; Hao Fang; Patrick Xia; Eran Levy; Jacob Andreas; Benjamin Van Durme; |
| 66 | PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional segmentation algorithms falter as they cannot accurately mimic the complexity of UAV perspectives, and the cost of obtaining multi-perspective labeled datasets is prohibitive. To address these issues, we introduce the PPTFormer, a novel Pseudo Multi-Perspective Transformer network that revolutionizes UAV image segmentation. |
Deyi Ji; Wenwei Jin; Hongtao Lu; Feng Zhao; |
| 67 | MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. |
Haicheng Liao; Zhenning Li; Chengyue Wang; Huanming Shen; Dongping Liao; Bonan Wang; Guofa Li; Chengzhong Xu; |
| 68 | Physics-Informed Trajectory Prediction for Autonomous Driving Under Missing Observation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel trajectory prediction approach for autonomous vehicles (AVs), adeptly addressing the challenges of missing observations and the need for adherence to physical laws in real-world driving environments. |
Haicheng Liao; Chengyue Wang; Zhenning Li; Yongkang Li; Bonan Wang; Guofa Li; Chengzhong Xu; |
| 69 | Interactive Visual Learning for Stable Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts into images. |
Seongmin Lee; Benjamin Hoover; Hendrik Strobelt; Zijie J. Wang; ShengYun Peng; Austin Wright; Kevin Li; Haekyu Park; Haoyang Yang; Duen Horng Chau; |
| 70 | Cross-Scale Domain Adaptation with Comprehensive Information for Pansharpening Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It limits their performance when generalizing the trained model to the full-resolution scale due to incomprehensive information utilization of panchromatic (PAN) images at the full-resolution scale and low generalization ability. In this paper, we adopt two targeted strategies to address the above two problems. |
Meiqi Gong; Hao Zhang; Hebaixu Wang; Jun Chen; Jun Huang; Xin Tian; Jiayi Ma; |
| 71 | Towards Geometric Normalization Techniques in SE(3) Equivariant Graph Neural Networks for Physical Dynamics Simulations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new normalization layer called GeoNorm, which can satisfy the SE(3) equivariance and simultaneously stabilize the training process. |
Ziqiao Meng; Liang Zeng; Zixing Song; Tingyang Xu; Peilin Zhao; Irwin King; |
| 72 | Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Due to the intrinsic complexity of algorithms, effective methods for universally extracting algorithm information are lacking. This paper takes a significant step towards bridging this gap by introducing Large Language Models (LLMs) into algorithm selection for the first time. |
Xingyu Wu; Yan Zhong; Jibin Wu; Bingbing Jiang; Kay Chen Tan; |
| 73 | MAS-SAM: Segment Any Marine Animal with Aggregated Features Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Meanwhile, the simplicity of the SAM’s decoder might lead to the loss of fine-grained object details. To address the above issues, we propose a novel feature learning framework named MAS-SAM for marine animal segmentation, which involves integrating effective adapters into the SAM’s encoder and constructing a pyramidal decoder. |
Tianyu Yan; Zifu Wan; Xinhao Deng; Pingping Zhang; Yang Liu; Huchuan Lu; |
| 74 | A Systematic Survey on Federated Semi-supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper systematically explores FSSL, shedding light on its four basic problem settings that commonly appear in real-world scenarios. By examining the unique challenges, generic solutions, and representative methods tailored for each setting of FSSL, we aim to provide a cohesive overview of the current state of the art and pave the way for future research directions in this promising field. |
Zixing Song; Xiangli Yang; Yifei Zhang; Xinyu Fu; Zenglin Xu; Irwin King; |
| 75 | NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to develop a modern approach to answering questions via systematic reasoning where answers are supported by human interpretable proof trees grounded in an NL corpus of facts. |
Nathaniel Weir; Peter Clark; Benjamin Van Durme; |
| 76 | SeeDRec: Sememe-based Diffusion for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these prevailing endeavors commonly implement diffusion on item indices, leading to the increasing time complexity, the lack of transferability, and the inability to fully harness item semantic information. To tackle these challenges, we propose SeeDRec, a sememe-based diffusion framework for sequential recommendation (SR). |
Haokai Ma; Ruobing Xie; Lei Meng; Yimeng Yang; Xingwu Sun; Zhanhui Kang; |
| 77 | FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a flexible and generalizable RL framework for VNE, named FlagVNE. |
Tianfu Wang; Qilin Fan; Chao Wang; Long Yang; Leilei Ding; Nicholas Jing Yuan; Hui Xiong; |
| 78 | CMACE: CMAES-based Counterfactual Explanations for Black-box Models Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Explanatory Artificial Intelligence plays a vital role in machine learning, due to its widespread application in decision-making scenarios, e.g., credit lending. Counterfactual … |
Xudong Yin; Yao Yang; |
| 79 | Enhancing Multimodal Knowledge Graph Representation Learning Through Triple Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite these advancements, existing multimodal fusion techniques still face significant challenges in representing modalities and fully integrating the diverse attributes of entities, particularly when dealing with more than one modality. To address this issue, this article proposes a Knowledge Graph Multimodal Representation Learning (KG-MRI) method. |
Yuxing Lu; Weichen Zhao; Nan Sun; Jinzhuo Wang; |
| 80 | Hierarchical Reinforcement Learning on Multi-Channel Hypergraph Neural Network for Course Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While achieving promising performances, current works suffering from the vary across the users and other MOOC entities. To address this problem, we propose hierarchical reinforcement learning with a multi-channel hypergraphs neural network for course recommendation(called HHCoR). |
Lu Jiang; Yanan Xiao; Xinxin Zhao; Yuanbo Xu; Shuli Hu; Pengyang Wang; Minghao Yin; |
| 81 | FairReFuse: Referee-Guided Fusion for Multi-Modal Causal Fairness in Depression Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a causal multimodal framework which consists of two modules. |
Jiaee Cheong; Sinan Kalkan; Hatice Gunes; |
| 82 | MediTab: Scaling Medical Tabular Data Predictors Via Data Consolidation, Enrichment, and Refinement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes to scale medical tabular data predictors (MediTab) to various tabular inputs with varying features. |
Zifeng Wang; Chufan Gao; Cao Xiao; Jimeng Sun; |
| 83 | FastScene: Text-Driven Fast Indoor 3D Scene Generation Via Panoramic Gaussian Splatting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, these methods often rely on narrow-field viewpoint iterative generations, compromising global consistency and overall scene quality. To address these issues, we propose FastScene, a framework for fast and higher-quality 3D scene generation, while maintaining the scene consistency. |
Yikun Ma; Dandan Zhan; Zhi Jin; |
| 84 | KG-CoT: Chain-of-Thought Prompting of Large Language Models Over Knowledge Graphs for Knowledge-Aware Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, fragmented knowledge facts extracted by knowledge retrievers fail to provide explicit and coherent reasoning paths for improving LLM reasoning. To address these challenges, we propose KG-CoT, a novel knowledge-augmented paradigm that leverages a small-scale step-by-step graph reasoning model to reason over knowledge graphs (KGs) and utilizes a reasoning path generation method to generate chains of reasoning with high confidence for large-scale LLMs. |
Ruilin Zhao; Feng Zhao; Long Wang; Xianzhi Wang; Guandong Xu; |
| 85 | The Information Retrieval Experiment Platform (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We have built TIREx, the information retrieval experiment platform, to promote standardized, reproducible, scalable, and blinded retrieval experiments. |
Maik Fröbe; Jan Heinrich Reimer; Sean MacAvaney; Niklas Deckers; Simon Reich; Janek Bevendorff; Benno Stein; Matthias Hagen; Martin Potthast; |
| 86 | Zero-shot High-fidelity and Pose-controllable Character Animation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they require a large amount of video data for training, which can be computationally demanding. To address these limitations, we propose PoseAnimate, a novel zero-shot I2V framework for character animation. |
Bingwen Zhu; Fanyi Wang; Tianyi Lu; Peng Liu; Jingwen Su; Jinxiu Liu; Yanhao Zhang; Zuxuan Wu; Guo-Jun Qi; Yu-Gang Jiang; |
| 87 | UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a unified multimodal 3D open-vocabulary scene understanding network, namely UniM-OV3D, aligning point clouds with image, language and depth. |
Qingdong He; Jinlong Peng; Zhengkai Jiang; Kai Wu; Xiaozhong Ji; Jiangning Zhang; Yabiao Wang; Chengjie Wang; Mingang Chen; Yunsheng Wu; |
| 88 | FastSAG: Towards Fast Non-Autoregressive Singing Accompaniment Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to develop a Fast SAG method that can create high-quality and coherent accompaniments. |
Jianyi Chen; Wei Xue; Xu Tan; Zhen Ye; Qifeng Liu; Yike Guo; |
| 89 | LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network Through Spatial-Temporal Compressive Network Search and Joint Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to design lightweight and efficient SNNs, we propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process. |
Qianhui Liu; Jiaqi Yan; Malu Zhang; Gang Pan; Haizhou Li; |
| 90 | Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current Event Stream Super-Resolution (ESR) methods overlook the redundant and complementary information present in positive and negative events within the event stream, employing a direct mixing approach for super-resolution, which may lead to detail loss and inefficiency. To address these issues, we propose an efficient Recursive Multi-Branch Information Fusion Network (RMFNet) that separates positive and negative events for complementary information extraction, followed by mutual supplementation and refinement. |
Quanmin Liang; Zhilin Huang; Xiawu Zheng; Feidiao Yang; Jun Peng; Kai Huang; Yonghong Tian; |
| 91 | DGCD: An Adaptive Denoising GNN for Group-level Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose DGCD, an adaptive Denoising graph neural network for realizing effective Group-level Cognitive Diagnosis. |
Haiping Ma; Siyu Song; Chuan Qin; Xiaoshan Yu; Limiao Zhang; Xingyi Zhang; Hengshu Zhu; |
| 92 | Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current lyric-to-melody generation methods struggle with the lack of paired lyric-melody data to train, and the lack of adherence to composition guidelines, resulting in melodies that do not sound human-composed. To address these issues, we propose a novel paradigm called Re-creation of Creations (ROC) that combines the strengths of both rule-based and neural-based methods. |
Ang Lv; Xu Tan; Tao Qin; Tie-Yan Liu; Rui Yan; |
| 93 | Reconstructing Missing Variables for Multivariate Time Series Forecasting Via Conditional Generative Flows Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing approaches towards these issues either heavily rely on local temporal correlation or face limitations in fully recovering missing information from the unavailable subset, accompanied by notable computational expenses. To address these problems, we propose a novel density estimation solution to recover the information of missing variables via flows-based generative framework. |
Xuanming Hu; Wei Fan; Haifeng Chen; Pengyang Wang; Yanjie Fu; |
| 94 | RealDex: Towards Human-like Grasping for Robotic Dexterous Hand Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns, enriched by multi-view and multimodal visual data. |
Yumeng Liu; Yaxun Yang; Youzhuo Wang; Xiaofei Wu; Jiamin Wang; Yichen Yao; Sören Schwertfeger; Sibei Yang; Wenping Wang; Jingyi Yu; Xuming He; Yuexin Ma; |
| 95 | Reinforcement Learning for Athletic Intelligence: Lessons from The 1st “AI Olympics with RealAIGym” Competition Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As artificial intelligence gains new capabilities, itbecomes important to evaluate it on real-worldtasks. In particular, the fields of robotics and reinforcement learning (RL) are … |
Felix Wiebe; Niccolò Turcato; Alberto Dalla Libera; Chi Zhang; Theo Vincent; Shubham Vyas; Giulio Giacomuzzo; Ruggero Carli; Diego Romeres; Akhil Sathuluri; Markus Zimmermann; Boris Belousov; Jan Peters; Frank Kirchner; Shivesh Kumar; |
| 96 | TIM: An Efficient Temporal Interaction Module for Spiking Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite their progress, a discernible gap exists in these systems, specifically in the Spiking Self Attention (SSA) mechanism’s effectiveness in leveraging the temporal processing potential of SNNs. To address this, we introduce the Temporal Interaction Module (TIM), a novel, convolution-based enhancement designed to augment the temporal data processing abilities within SNN architectures. |
Sicheng Shen; Dongcheng Zhao; Guobin Shen; Yi Zeng; |
| 97 | More Is Better: Deep Domain Adaptation with Multiple Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the … |
Sicheng Zhao; Hui Chen; Hu Huang; Pengfei Xu; Guiguang Ding; |
| 98 | Federated Prompt Learning for Weather Foundation Models on Devices Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, it faces three main challenges that hinder its reliability: (1) data heterogeneity among devices due to geographic differences; (2) data homogeneity within individual devices and (3) communication overload from sending large model parameters for collaboration. To address these challenges, this paper propose Federated Prompt learning for Weather Foundation Models on Devices (FedPoD), which enables devices to obtain highly customized models while maintaining communication efficiency. |
Shengchao Chen; Guodong Long; Tao Shen; Jing Jiang; Chengqi Zhang; |
| 99 | ParsNets: A Parsimonious Composition of Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL), dubbed ParsNets, in which we are interested in learning a composition of on-device friendly linear networks, each with orthogonality and low-rankness properties, to achieve equivalent or better performance against deep models. |
Jingcai Guo; Qihua Zhou; Xiaocheng Lu; Ruibin Li; Ziming Liu; Jie Zhang; Bo Han; Junyang Chen; Xin Xie; Song Guo; |
| 100 | FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client’s model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. |
Liping Yi; Han Yu; Zhuan Shi; Gang Wang; Xiaoguang Liu; Lizhen Cui; Xiaoxiao Li; |
| 101 | DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nighttime light (NTL) remote sensing observation serves as a unique proxy for quantitatively assessing progress toward meeting a series of Sustainable Development Goals (SDGs), such as poverty estimation, urban sustainable development, and carbon emission. |
Lixian Zhang; Runmin Dong; Shuai Yuan; Jinxiao Zhang; Mengxuan Chen; Juepeng Zheng; Haohuan Fu; |
| 102 | LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue, we propose a new method called LongVQ. |
Zicheng Liu; Li Wang; Siyuan Li; Zedong Wang; Haitao Lin; Stan Z. Li; |
| 103 | Nukplex: An Efficient Local Search Algorithm for Maximum K-Plex Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, to further improve the performance of solving the MKPP, we propose an efficient local search algorithm based on three main ideas. |
Rui Sun; Yiyuan Wang; Shimao Wang; Hui Li; Ximing Li; Minghao Yin; |
| 104 | Efficient Multi-view Unsupervised Feature Selection with Adaptive Structure Learning and Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Generally, existing methods encounter the following challenges: (i) traditional solutions either concatenate different views or introduce extra parameters to weight them, affecting the performance and applicability; (ii) emphasis is typically placed on graph construction, yet disregarding the clustering information of data; (iii) exploring the similarity structure of all samples from the original features is suboptimal and extremely time-consuming. To solve this dilemma, we propose an efficient multi-view unsupervised feature selection (EMUFS) to construct bipartite graphs between samples and anchors. |
Chenglong Zhang; Yang Fang; Xinyan Liang; Han Zhang; Peng Zhou; Xingyu Wu; Jie Yang; Bingbing Jiang; Weiguo Sheng; |
| 105 | Fast One-Stage Unsupervised Domain Adaptive Person Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods dedicate to developing multi-stage models to adapt domain variations while using clustering for iterative model training, which inevitably increase model complexity. To address this issue, we propose a Fast One-stage Unsupervised person Search (FOUS) which complementaryly integrates domain adaption with label adaption within an end-to-end manner without iterative clustering. |
Tianxiang Cui; Huibing Wang; Jinjia Peng; Ruoxi Deng; Xianping Fu; Yang Wang; |
| 106 | A Survey of Robotic Language Grounding: Tradeoffs Between Symbols and Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Using a formal representation allows the meaning of the language to be precisely represented, limits the size of the learning problem, and leads to a framework for interpretability and formal safety guarantees. Methods that embed language and perceptual data into high-dimensional spaces avoid this manually specified symbolic structure and thus have the potential to be more general when fed enough data but require more data and computing to train. |
Vanya Cohen; Jason Xinyu Liu; Raymond Mooney; Stefanie Tellex; David Watkins; |
| 107 | Recent Advances in Predictive Modeling with Electronic Health Records Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce the background of EHR data and provide a mathematical definition of the predictive modeling task. |
Jiaqi Wang; Junyu Luo; Muchao Ye; Xiaochen Wang; Yuan Zhong; Aofei Chang; Guanjie Huang; Ziyi Yin; Cao Xiao; Jimeng Sun; Fenglong Ma; |
| 108 | A General Black-box Adversarial Attack on Graph-based Fake News Detectors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. |
Peican Zhu; Zechen Pan; Yang Liu; Jiwei Tian; Keke Tang; Zhen Wang; |
| 109 | A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a comprehensive survey and taxonomy of recently proposed PCR methods. |
Yu-Xin Zhang; Jie Gui; Xiaofeng Cong; Xin Gong; Wenbing Tao; |
| 110 | Large Language Models for Time Series: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We address the inherent challenge of bridging the gap between LLMs’ original text data training and the numerical nature of time series data, and explore strategies for transferring and distilling knowledge from LLMs to numerical time series analysis. |
Xiyuan Zhang; Ranak Roy Chowdhury; Rajesh K. Gupta; Jingbo Shang; |
| 111 | Attention Based Document-level Relation Extraction with None Class Ranking Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, current methods only independently estimate the cases of predefined relations, ignoring the case of "no relation”, which results in poor prediction. To address the above issues, we propose a document-level RE method based on attention mechanisms, which considers the case of "no relation”. |
Xiaolong Xu; Chenbin Li; Haolong Xiang; Lianyong Qi; Xuyun Zhang; Wanchun Dou; |
| 112 | An Image-enhanced Molecular Graph Representation Learning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we fully consider the rich visual information contained in 3D conformation molecular images (i.e., texture, shadow, color and planar spatial information) and distill graph-based models for more discriminative drug discovery. |
Hongxin Xiang; Shuting Jin; Jun Xia; Man Zhou; Jianmin Wang; Li Zeng; Xiangxiang Zeng; |
| 113 | Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Trajectory modeling refers to characterizing human movement behavior, serving as a pivotal step in understanding mobility patterns. Nevertheless, existing studies typically ignore … |
Kang Luo; Yuanshao Zhu; Wei Chen; Kun Wang; Zhengyang Zhou; Sijie Ruan; Yuxuan Liang; |
| 114 | Hierarchical Reinforcement Learning for Point of Interest Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although existing technologies are proficient in processing time-series data, they fall short when it comes to accommodating the diversity and dynamism in users’ POI selections, particularly in extracting key signals from complex historical behaviors. To address this challenge, we introduced the Hierarchical Reinforcement Learning Preprocessing Framework (HRL-PRP), a framework that can be integrated into existing recommendation models to effectively optimize user profiles. |
Yanan Xiao; Lu Jiang; Kunpeng Liu; Yuanbo Xu; Pengyang Wang; Minghao Yin; |
| 115 | Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce Micre (Meta In-Context learning of LLMs for Relation Extraction), a new meta-training framework for zero and few-shot RE where an LLM is tuned to do ICL on a diverse collection of RE datasets (i.e., learning to learn in context for RE). |
Guozheng Li; Peng Wang; Jiajun Liu; Yikai Guo; Ke Ji; Ziyu Shang; Zijie Xu; |
| 116 | Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel recall-retrieve-reason RE framework that synergizes LLMs with retrieval corpora (training examples) to enable relevant retrieving and reliable in-context reasoning. |
Guozheng Li; Peng Wang; Wenjun Ke; Yikai Guo; Ke Ji; Ziyu Shang; Jiajun Liu; Zijie Xu; |
| 117 | Cross-Problem Learning for Solving Vehicle Routing Problems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes the cross-problem learning to assist heuristics training for different downstream VRP variants. |
Zhuoyi Lin; Yaoxin Wu; Bangjian Zhou; Zhiguang Cao; Wen Song; Yingqian Zhang; Senthilnath Jayavelu; |
| 118 | Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we argue that current watermarking models, originally devised for genuine images, may harm the deployed Deepfake detectors when directly applied to forged images, since the watermarks are prone to overlap with the forgery signals used for detection. To bridge this gap, we thus propose AdvMark, on behalf of proactive forensics, to exploit the adversarial vulnerability of passive detectors for good. |
Xiaoshuai Wu; Xin Liao; Bo Ou; Yuling Liu; Zheng Qin; |
| 119 | Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a new approach to federated learning that allows selecting a model from global and personalized ones that would perform better for a particular input point. |
Nikita Kotelevskii; Samuel Horváth; Karthik Nandakumar; Martin Takac; Maxim Panov; |
| 120 | Recent Advances in End-to-End Simultaneous Speech Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Finally, the scarcity of annotated data adds another layer of complexity to the task. Through our exploration of these challenges and the proposed solutions, we aim to provide valuable insights into the current landscape of SimulST research and suggest promising directions for future exploration. |
Xiaoqian Liu; Guoqiang Hu; Yangfan Du; Erfeng He; YingFeng Luo; Chen Xu; Tong Xiao; Jingbo Zhu; |
| 121 | Make Bricks with A Little Straw: Large-Scale Spatio-Temporal Graph Learning with Restricted GPU-Memory Capacity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take the first step of learning on the large-scale spatio-temporal graph and propose a divide-and-conquer training strategy for Large Spatio-Temporal Graph Learning, namely LarSTL. |
Binwu Wang; Pengkun Wang; Zhengyang Zhou; Zhe Zhao; Wei Xu; Yang Wang; |
| 122 | CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce CMMU, a novel benchmark for multi-modal and multi-type question understanding and reasoning in Chinese. |
Zheqi He; Xinya Wu; Pengfei Zhou; Richeng Xuan; Guang Liu; Xi Yang; Qiannan Zhu; Hua Huang; |
| 123 | Fuel-Saving Route Planning with Data-Driven and Learning-Based Approaches – A Systematic Solution for Harbor Tugs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, an innovative learning-based method, comprising a Reinforcement Learning (RL) model together with a fuel consumption prediction model, was proposed to formulate fuel-saving transit routes. |
Shengming Wang; Xiaocai Zhang; Jing Li; Xiaoyang Wei; Hoong Chuin Lau; Bing Tian Dai; Binbin Huang; Zhe Xiao; Xiuju Fu; Zheng Qin; |
| 124 | Self-Supervised Monocular Depth Estimation in The Dark: Towards Data Distribution Compensation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a self-supervised nighttime monocular depth estimation method that does not use any night images during training. |
Haolin Yang; Chaoqiang Zhao; Lu Sheng; Yang Tang; |
| 125 | SaSDim:Self-Adaptive Noise Scaling Diffusion Model for Spatial Time Series Imputation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a Self-adaptive noise Scaling Diffusion Model named SaSDim for spatial time series imputation. |
Shunyang Zhang; Senzhang Wang; Xianzhen Tan; Renzhi Wang; Ruochen Liu; Jian Zhang; Jianxin Wang; |
| 126 | IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the effective workflow of professional human market makers, we propose Imitative Market Maker (IMM), a novel RL framework leveraging knowledge from both suboptimal signal-based experts and direct policy interactions. |
Hui Niu; Siyuan Li; Jiahao Zheng; Zhouchi Lin; Bo An; Jian Li; Jian Guo; |
| 127 | ChatSpot: Bootstrapping Multimodal LLMs Via Precise Referring Instruction Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region. |
Liang Zhao; En Yu; Zheng Ge; Jinrong Yang; Haoran Wei; Hongyu Zhou; Jianjian Sun; Yuang Peng; Runpei Dong; Chunrui Han; Xiangyu Zhang; |
| 128 | CF-Deformable DETR: An End-to-End Alignment-Free Model for Weakly Aligned Visible-Infrared Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Weakly aligned visible-infrared object detection poses significant challenges due to the imprecise alignment between visible and infrared images. Most existing methods explore the … |
Haolong Fu; Jin Yuan; Guojin Zhong; Xuan He; Jiacheng Lin; Zhiyong Li; |
| 129 | AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the quadratic complexity of self-attention mechanism in GTs has limited their scalability, and previous approaches to address this issue often suffer from expressiveness degradation or lack of versatility. To address this issue, we propose AnchorGT, a novel attention architecture for GTs with global receptive field and almost linear complexity, which serves as a flexible building block to improve the scalability of a wide range of GT models. |
Wenhao Zhu; Guojie Song; Liang Wang; Shaoguo Liu; |
| 130 | Modeling Personalized Retweeting Behaviors for Multi-Stage Cascade Popularity Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a universal cascade prediction framework, namely Cascade prediction regarding Multiple Stage (CasMS), that effectively predicts cascade popularity across message generation stage as well as short-term and long-term stages. |
Mingyang Zhou; Yanjie Lin; Gang Liu; Zuwen Li; Hao Liao; Rui Mao; |
| 131 | Boosting Model Resilience Via Implicit Adversarial Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, consistently improving model performance in varied learning scenarios, especially those with inherent data biases, remains challenging. To address this, we propose to augment the deep features of samples by incorporating their adversarial and anti-adversarial perturbation distributions, enabling adaptive adjustment in the learning difficulty tailored to each sample’s specific characteristics. |
Xiaoling Zhou; Wei Ye; Zhemg Lee; Rui Xie; Shikun Zhang; |
| 132 | Spatio-Temporal Field Neural Networks for Air Quality Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While spatio-temporal graph neural networks have made excellent progress on this problem, their non-Euclidean and discrete data structure modeling of reality limits its potential. In this work, we make the first attempt to combine two different spatio-temporal perspectives, fields and graphs, by proposing a new model, Spatio-Temporal Field Neural Network, and its corresponding new framework, Pyramidal Inference. |
Yutong Feng; Qiongyan Wang; Yutong Xia; Junlin Huang; Siru Zhong; Yuxuan Liang; |
| 133 | Integrating View Conditions for Image Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a pioneering framework that integrates viewpoint information to enhance the control of image editing tasks, especially for interior design scenes. |
Jinbin Bai; Zhen Dong; Aosong Feng; Xiao Zhang; Tian Ye; Kaicheng Zhou; |
| 134 | Safeguarding Fraud Detection from Attacks: A Robust Graph Learning Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, most existing attack-defense models tend to study on ideal settings and lose information during truncation or filtering, which lowers their performances in complicated financial fraud cases. Therefore, in this paper, we propose a novel robust anti-fraud GNN model. |
Jiasheng Wu; Xin Liu; Dawei Cheng; Yi Ouyang; Xian Wu; Yefeng Zheng; |
| 135 | From Optimization to Generalization: Fair Federated Learning Against Quality Shift Via Inter-Client Sharpness Matching Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This imbalance in image quality can cause the federated model to develop an inherent bias towards higher-quality images, thus posing a severe fairness issue. In this study, we pioneer the identification and formulation of this new fairness challenge within the context of the imaging quality shift. |
Nannan Wu; Zhuo Kuang; Zengqiang Yan; Li Yu; |
| 136 | PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Observing that graph neural network (GNN) performs well as both encoder and predictor, we propose PACIA, a parameter-efficient GNN adapter for few-shot MPP. |
Shiguang Wu; Yaqing Wang; Quanming Yao; |
| 137 | Aggregation and Purification: Dual Enhancement Network for Point Cloud Few-shot Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design a novel Dual Enhancement Network (DENet) to comprehensively tackle different kinds of scene discrepancies in a coherent and synergistic framework. |
Guoxin Xiong; Yuan Wang; Zhaoyang Li; Wenfei Yang; Tianzhu Zhang; Xu Zhou; Shifeng Zhang; Yongdong Zhang; |
| 138 | Guiding Clinical Reasoning with Large Language Models Via Knowledge Seeds Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we introduce a novel framework, In-Context Padding (ICP), to enhance LLMs reasoning with medical knowledge. |
Jiageng Wu; Xian Wu; Jie Yang; |
| 139 | BATON: Aligning Text-to-Audio Model Using Human Preference Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is challenging for these models to generate audio aligned with human preference due to the inherent information density of natural language and limited model understanding ability. To alleviate this issue, we formulate the BATON, the first framework specifically designed to enhance the alignment between generated audio and text prompt using human preference feedback. |
Huan Liao; Haonan Han; Kai Yang; Tianjiao Du; Rui Yang; Qinmei Xu; Zunnan Xu; Jingquan Liu; Jiasheng Lu; Xiu Li; |
| 140 | Label-efficient Semantic Scene Completion with Scribble Annotations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we build a new label-efficient benchmark, named ScribbleSC, where the sparse scribble-based semantic labels are combined with dense geometric labels for semantic scene completion. |
Song Wang; Jiawei Yu; Wentong Li; Hao Shi; Kailun Yang; Junbo Chen; Jianke Zhu; |
| 141 | Spear: Evaluate The Adversarial Robustness of Compressed Neural Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A specific adversarial attack method (Spear) is proposed to generate the particular adversarial attack samples for evaluating the robustness of the compressed models. |
Chong Yu; Tao Chen; Zhongxue Gan; Jiayuan Fan; |
| 142 | LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Different from them, this paper proposes to use learnable meta tokens to formulate sparse tokens, which effectively learn key information meanwhile improving the inference speed. |
Wentao Jiang; Jing Zhang; Di Wang; Qiming Zhang; Zengmao Wang; Bo Du; |
| 143 | Metric Distortion with Elicited Pairwise Comparisons Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main contributions are nearly optimal algorithms for all settings considered. |
Soroush Ebadian; Daniel Halpern; Evi Micha; |
| 144 | Unified Evidence Enhancement Inference Framework for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose Unified Evidence Enhancement Inference framework (UEEI) to discover and infer high-quality evidence to reveal the false parts of news for detection. |
Lianwei Wu; Linyong Wang; Yongqiang Zhao; |
| 145 | Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning. |
Luigi Bonassi; Giuseppe De Giacomo; Marco Favorito; Francesco Fuggitti; Alfonso Emilio Gerevini; Enrico Scala; |
| 146 | Efficient Screen Content Image Compression Via Superpixel-based Content Aggregation and Dynamic Feature Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Superpixel-based Content Aggregation Block (SCAB) to aggregate local pixels into one super-pixel and aggregate non-local information via super-pixel transformer. |
Sheng Shen; Huanjing Yue; Jingyu Yang; |
| 147 | P2P: Transforming from Point Supervision to Explicit Visual Prompt for Object Detection and Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel iterative learning framework, Point to Prompt (P2P), for point-supervised object detection and segmentation, with the key insight of transforming from point supervision to explicit visual prompt of the foundation model. |
Guangqian Guo; Dian Shao; Chenguang Zhu; Sha Meng; Xuan Wang; Shan Gao; |
| 148 | Multi-Modality Spatio-Temporal Forecasting Via Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose a novel MoST learning framework via Self-Supervised Learning, namely MoSSL, which aims to uncover latent patterns from temporal, spatial, and modality perspectives while quantifying dynamic heterogeneity. |
Jiewen Deng; Renhe Jiang; Jiaqi Zhang; Xuan Song; |
| 149 | Scalable Mechanism Design for Multi-Agent Path Finding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms. |
Paul Friedrich; Yulun Zhang; Michael Curry; Ludwig Dierks; Stephen McAleer; Jiaoyang Li; Tuomas Sandholm; Sven Seuken; |
| 150 | 1DFormer: A Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous methods ignored to make deep explorations on the good potentials of 1D landmark representations for sequential and structural modeling of multiple landmarks to track facial landmarks. To address this limitation, we propose a Transformer architecture, namely 1DFormer, which learns informative 1D landmark representations by capturing the dynamic and the geometric patterns of landmarks via token communications in both temporal and spatial dimensions for facial landmark tracking. |
Shi Yin; Shijie Huang; Shangfei Wang; Jinshui Hu; Tao Guo; Bing Yin; Baocai Yin; Cong Liu; |
| 151 | ProMoAI: Process Modeling with Generative AI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques. |
Humam Kourani; Alessandro Berti; Daniel Schuster; Wil M.P. van der Aalst; |
| 152 | The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper explores the preliminary design of federated intelligence that paves the way toward personalized intelligent agents in large-scale collaboration scenarios. |
Guodong Long; |
| 153 | Game Transformations That Preserve Nash Equilibria or Best-Response Sets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate under which conditions normal-form games are (guaranteed to be) strategically equivalent. |
Emanuel Tewolde; Vincent Conitzer; |
| 154 | Imperfect-Recall Games: Equilibrium Concepts and Their Complexity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the framework of extensive-form games with imperfect recall, we analyze the computational complexities of finding equilibria in multiplayer settings across three different solution concepts: Nash, multiselves based on evidential decision theory (EDT), and multiselves based on causal decision theory (CDT). |
Emanuel Tewolde; Brian Hu Zhang; Caspar Oesterheld; Manolis Zampetakis; Tuomas Sandholm; Paul Goldberg; Vincent Conitzer; |
| 155 | QFormer: An Efficient Quaternion Transformer for Image Denoising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Secondly, the DCNNs or Transformer-based image denoising models usually have a large number of parameters, high computational complexity, and slow inference speed. To resolve these issues, this paper proposes a highly-efficient Quaternion Transformer (QFormer) for image denoising. |
Bo Jiang; Yao Lu; Guangming Lu; Bob Zhang; |
| 156 | HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel hybrid voxel-octree approach to effectively fuse octree with voxel structures so that we can take advantage of both implicit surface and explicit triangular mesh representation. |
Shaofan Liu; Junbo Chen; Jianke Zhu; |
| 157 | Predicting Carpark Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we aim to collectively predict future PA across Singapore with complex factors from various domains. |
Huaiwu Zhang; Yutong Xia; Siru Zhong; Kun Wang; Zekun Tong; Qingsong Wen; Roger Zimmermann; Yuxuan Liang; |
| 158 | Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the absence of dynamicity modeling, such approaches are vulnerable to evasion, particularly when advanced social bots interact with other users to camouflage identities and escape detection. To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network. |
Buyun He; Yingguang Yang; Qi Wu; Hao Liu; Renyu Yang; Hao Peng; Xiang Wang; Yong Liao; Pengyuan Zhou; |
| 159 | Dynamic Weighted Graph Fusion for Deep Multi-View Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although considerable progress has been made, most existing GNN based MVC models merely consider the explicit presence of graph structure in raw data and ignore that latent graphs of different views also provide specific information for the clustering task. We propose dynamic weighted graph fusion for deep multi-view clustering (DFMVC) to address this issue. |
Yazhou Ren; Jingyu Pu; Chenhang Cui; Yan Zheng; Xinyue Chen; Xiaorong Pu; Lifang He; |
| 160 | How to Learn Domain-Invariant Representations for Visual Reinforcement Learning: An Information-Theoretical Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore how to learn domain-invariant representations for VRL from an information-theoretical perspective. |
Shuo Wang; Zhihao Wu; Jinwen Wang; Xiaobo Hu; Youfang Lin; Kai Lv; |
| 161 | Where Elegance Meets Precision: Towards A Compact, Automatic, and Flexible Framework for Multi-modality Image Fusion and Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such simplistic strategies struggle to truly achieve the "Best of Both Worlds", and the adjustment of numerous hand-crafted parameters becomes burdensome. To address these challenges, this paper introduces a Compact, Automatic and Flexible framework, dubbed CAF, designed for infrared and visible image fusion, along with subsequent tasks. |
Jinyuan Liu; Guanyao Wu; Zhu Liu; Long Ma; Risheng Liu; Xin Fan; |
| 162 | Trustworthy Machine Learning Under Imperfect Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, there are mainly three types of imperfect data along with their challenges for ML, including i) label-level imperfection: noisy labels; ii) feature-level imperfection: adversarial examples; iii) distribution-level imperfection: out-of-distribution data. Therefore, in this paper, we systematically share our insights and solutions of TML to handle three types of imperfect data. |
Bo Han; |
| 163 | FedFa: A Fully Asynchronous Training Paradigm for Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a full asynchronous training paradigm called FedFa, which can guarantee model convergence and eliminate the waiting time completely for federated learning by using a few buffered results on the server for parameter updating. |
Haotian Xu; Zhaorui Zhang; Sheng Di; Benben Liu; Khalid Ayed Alharthi; Jiannong Cao; |
| 164 | Predictive Modeling with Temporal Graphical Representation on Electronic Health Records Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The sequential representation methods focus only on the temporal relationships among longitudinal visits. |
Jiayuan Chen; Changchang Yin; Yuanlong Wang; Ping Zhang; |
| 165 | Searching for Programmatic Policies in Semantic Spaces Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an alternative method for synthesizing programmatic policies, where we search within an approximation of the language’s semantic space. |
Rubens O. Moraes; Levi H. S. Lelis; |
| 166 | Effective High-order Graph Representation Learning for Credit Card Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing graph neural network (GNN) models struggle with learning features of camouflaged, indirect multi-hop transactions due to their inherent over-smoothing issues in deep multi-layer aggregation, presenting a major challenge in detecting disguised relationships. Therefore, in this paper, we propose a novel High-order Graph Representation Learning model (HOGRL) to avoid incorporating excessive noise during the multi-layer aggregation process. |
Yao Zou; Dawei Cheng; |
| 167 | EAT: Self-Supervised Pre-Training with Efficient Audio Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, inspired by the success of data2vec 2.0 in image modality and Audio-MAE in audio modality, we introduce Efficient Audio Transformer (EAT) to further improve the effectiveness and efficiency in audio SSL. |
Wenxi Chen; Yuzhe Liang; Ziyang Ma; Zhisheng Zheng; Xie Chen; |
| 168 | Multi-Attention Based Visual-Semantic Interaction for Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, for meta-learning purposes, the semantic knowledge of the query set is unavailable, so their features lack discriminability. To address this problem, we propose a novel Multi-Attention based Visual-Semantic Interaction (MAVSI) approach for FSL. |
Peng Zhao; Yin Wang; Wei Wang; Jie Mu; Huiting Liu; Cong Wang; Xiaochun Cao; |
| 169 | CDSTraj: Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. |
Haicheng Liao; Xuelin Li; Yongkang Li; Hanlin Kong; Chengyue Wang; Bonan Wang; Yanchen Guan; KaHou Tam; Zhenning Li; |
| 170 | GS2P: A Generative Pre-trained Learning to Rank Model with Over-parameterization for Web-Scale Search (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While Learning to Rank (LTR) is widely employed in web searches to prioritize pertinent webpages from the retrieved contents based on input queries, traditional LTR models stumble over two principal stumbling blocks leading to subpar performance: 1) the lack of well-annotated query-webpage pairs with ranking scores to cover search queries of various popularity, debilitating their coverage of search queries across the popularity spectrum, and 2) ill-trained models that are incapable of inducing generalized representations for LTR, culminating in overfitting. To tackle above challenges, we proposed a Generative Semi-supervised Pre-trained (GS2P) LTR model. |
Yuchen Li; Haoyi Xiong; Linghe Kong; Jiang Bian; Shuaiqiang Wang; Guihai Chen; Dawei Yin; |
| 171 | MPGraf: A Modular and Pre-trained Graphformer for Learning to Rank at Web-scale (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the novel MPGraf model, which utilizes a modular and capsule-based pre-training approach, aiming to incorporate regression capacities from Transformers and link prediction capabilities of GNNs cohesively. |
Yuchen Li; Haoyi Xiong; Linghe Kong; Zeyi Sun; Hongyang Chen; Shuaiqiang Wang; Dawei Yin; |
| 172 | VulnerabilityMap: An Open Framework for Mapping Vulnerability Among Urban Disadvantaged Populations in The United States Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, a comprehensive framework for studying this urban dilemma is currently absent, preventing researchers from developing AI models for social good prediction and intervention. To fill this gap, we construct VulnerabilityMap, a framework to meticulously dissect the challenges faced by urban disadvantaged populations, unraveling their vulnerability to a spectrum of shocks and stresses that are categorized through the prism of Maslow’s hierarchy of needs. |
Lin Chen; Yong Li; Pan Hui; |
| 173 | Online Combinatorial Optimization with Group Fairness Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this can be challenging as these processes often need to solve NP-complete problems with exponentially large decision spaces at each time step. To overcome this, we propose a general framework incorporating robustness and fairness into NP-complete problems, such as optimizing product ranking and maximizing sub-modular functions. |
Negin Golrezaei; Rad Niazadeh; Kumar Kshitij Patel; Fransisca Susan; |
| 174 | TFCD: Towards Multi-modal Sarcasm Detection Via Training-Free Counterfactual Debiasing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Concretely, such harmful biases are confounders that may mislead existing models to learn spurious correlations, significantly limiting models’ performance. To tackle this issue, this paper proposes a Training-Free Counterfactual Debiasing framework TFCD, which first formulates the causalities among variables in MSD via a tailored causal graph. |
Zhihong Zhu; Xianwei Zhuang; Yunyan Zhang; Derong Xu; Guimin Hu; Xian Wu; Yefeng Zheng; |
| 175 | AVIN-Chat: An Audio-Visual Interactive Chatbot System with Emotional State Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents an audio-visual interactive chatbot (AVIN-Chat) system that allows users to have face-to-face conversations with 3D avatars in real-time. |
Chanhyuk Park; Jungbin Cho; Junwan Kim; Seongmin Lee; Jungsu Kim; Sanghoon Lee; |
| 176 | DANCE: Dual-View Distribution Alignment for Dataset Condensation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we reveal the limitations of current DM-based methods from the inner-class and inter-class views, i.e., Persistent Training and Distribution Shift. |
Hansong Zhang; Shikun Li; Fanzhao Lin; Weiping Wang; Zhenxing Qian; Shiming Ge; |
| 177 | Optimal Graph Learning and Nuclear Norm Maximization for Deep Cross-Domain Robust Label Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce optimal graph learning to generate a cross-domain graph that effectively connects the two domains, and two domain-specific graphs to capture domain-specific structures. |
Wei Wang; Hanyang Li; Ke Shi; Chao Huang; Yang Cao; Cong Wang; Xiaochun Cao; |
| 178 | MacMic: Executing Iceberg Orders Via Hierarchical Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The latter increases the task complexity, since the agent must capture price advantages throughout the day as well as micro changes within a few seconds on the limited order books. In addressing these challenges, we propose MacMic, a novel Hierarchical RL-based order execution approach that captures market patterns and executes orders from different temporal scales. |
Hui Niu; Siyuan Li; Jian Li; |
| 179 | Trusted Multi-view Learning with Label Noise Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This motivates us to delve into a new generalized trusted multi-view learning problem: how to develop a reliable multi-view learning model under the guidance of noisy labels? We propose a trusted multi-view noise refining method to solve this problem. |
Cai Xu; Yilin Zhang; Ziyu Guan; Wei Zhao; |
| 180 | Graph Neural Networks for Brain Graph Learning: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. |
Xuexiong Luo; Jia Wu; Jian Yang; Shan Xue; Amin Beheshti; Quan Z. Sheng; David McAlpine; Paul Sowman; Alexis Giral; Philip S. Yu; |
| 181 | Unlearning During Learning: An Efficient Federated Machine Unlearning Method Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current FMU approaches often involve additional time-consuming steps and may not offer comprehensive unlearning capabilities, which renders them less practical in real FL scenarios. In this paper, we introduce FedAU, an innovative and efficient FMU framework aimed at overcoming these limitations. |
Hanlin Gu; Gongxi Zhu; Jie Zhang; Xinyuan Zhao; Yuxing Han; Lixin Fan; Qiang Yang; |
| 182 | Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, current end-to-end models are limited by input length and thus often fall into spatiotemporal mirage, i.e., similar input time series followed by dissimilar future values and vice versa. To address these problems, we propose a novel self-supervised pre-training framework Spatial-Temporal-Decoupled Masked Pre-training (STD-MAE) that employs two decoupled masked autoencoders to reconstruct spatiotemporal series along the spatial and temporal dimensions. |
Haotian Gao; Renhe Jiang; Zheng Dong; Jinliang Deng; Yuxin Ma; Xuan Song; |
| 183 | Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, traditional NLI models solely rely on the semantic information inherent in independent sentences and lack relevant situational visual information, which can hinder a complete understanding of the intended meaning of the sentences due to the ambiguity and vagueness of language. To address this challenge, we propose an innovative ScenaFuse adapter that simultaneously integrates large-scale pre-trained linguistic knowledge and relevant visual information for NLI tasks. |
Yonghao Liu; Mengyu Li; Di Liang; Ximing Li; Fausto Giunchiglia; Lan Huang; Xiaoyue Feng; Renchu Guan; |
| 184 | Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce the P2DTR framework, a novel approach in DTCDR while protecting private user information. |
Zhenghong Lin; Wei Huang; Hengyu Zhang; Jiayu Xu; Weiming Liu; Xinting Liao; Fan Wang; Shiping Wang; Yanchao Tan; |
| 185 | A Prior-information-guided Residual Diffusion Model for Multi-modal PET Synthesis from MRI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel unified model to simultaneously synthesize multi-modal PET images from MRI, to achieve low-cost and time-efficient joint multi-biomarker diagnosis of AD. |
Zaixin Ou; Caiwen Jiang; Yongsheng Pan; Yuanwang Zhang; Zhiming Cui; Dinggang Shen; |
| 186 | Welfare Loss in Connected Resource Allocation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce the concept of egalitarian (resp., utilitarian) price of connectivity, which captures the worst-case ratio between the optimal egalitarian (resp., utilitarian) welfare among all allocations and that among the connected allocations. |
Xiaohui Bei; Alexander Lam; Xinhang Lu; Warut Suksompong; |
| 187 | DGR: A General Graph Desmoothing Framework for Recommendation Via Global and Local Perspectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel, model-agnostic approach named Desmoothing Framework for GCN-based Recommendation Systems (DGR). |
Leilei Ding; Dazhong Shen; Chao Wang; Tianfu Wang; Le Zhang; Yanyong Zhang; |
| 188 | A Single Vector Is Not Enough: Taxonomy Expansion Via Box Embeddings (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose to project taxonomy entities into boxes (i.e., hyperrectangles). |
Song Jiang; Qiyue Yao; Qifan Wang; Yizhou Sun; |
| 189 | Capturing Knowledge Graphs and Rules with Octagon Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, existing approaches are severely restricted in their ability to model relational composition, and hence also their ability to model rules, thus failing to deliver on the main promise of region based models. With the aim of addressing these limitations, we investigate regions which are composed of axis-aligned octagons. |
Victor Charpenay; Steven Schockaert; |
| 190 | Fast Unpaired Multi-view Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The absence of pairing in multi-view data disrupts the consistency and complementarity of multiple views, posing significant challenges in learning powerful and meaningful anchors and bipartite graphs from unpaired multi-view data. To tackle this challenge, this study proposes a novel Fast Unpaired Multi-view Clustering (FUMC) framework for fully unpaired large-scale multi-view data. |
Xingfeng Li; Yuangang Pan; Yinghui Sun; Quansen Sun; Ivor Tsang; Zhenwen Ren; |
| 191 | GSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To counter overconfidence, we introduce Generalised Binary Cross-Entropy (gBCE) loss and the gSASRec model that utilises gBCE. |
Aleksandr V. Petrov; Craig Macdonald; |
| 192 | CGAP: Urban Region Representation Learning with Coarsened Graph Attention Pooling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, many existing GNN approaches encounter challenges such as over-smoothing and limitations in capturing information from nodes in other regions, resulting in the loss of crucial urban information and a decline in region representation performance. To address these challenges, we leverage urban graph structure information and introduce a hierarchical graph pooling process called Coarsened Graph Attention Pooling (CGAP). |
Zhuo Xu; Xiao Zhou; |
| 193 | BADFSS: Backdoor Attacks on Federated Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel backdoor attack BADFSS against SSL-based FL. |
Jiale Zhang; Chengcheng Zhu; Di Wu; Xiaobing Sun; Jianming Yong; Guodong Long; |
| 194 | Bridging The Gap Between General and Down-Closed Convex Sets in Submodular Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, a recent hardness result due to Mualem and Feldman shows that this approach cannot yield a smooth interpolation between down-closed and non-down-closed constraints. In this work, we suggest novel offline and online algorithms that provably provide such an interpolation based on a natural decomposition of the convex body constraint into two distinct convex bodies: a down-closed convex body and a general convex body. |
Loay Mualem; Murad Tukan; Moran Feldman; |
| 195 | Quality-Diversity Algorithms Can Provably Be Helpful for Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we try to shed some light on the optimization ability of QD algorithms via rigorous runtime analysis. |
Chao Qian; Ke Xue; Ren-Jian Wang; |
| 196 | Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We contend that studies of detecting AI-generated content within hybrid texts should cover different types of hybrid texts generated in realistic settings to better inform real-world applications. |
Zijie Zeng; Shiqi Liu; Lele Sha; Zhuang Li; Kaixun Yang; Sannyuya Liu; Dragan Gasevic; Guangliang Chen; |
| 197 | CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenge of open-set learning with complex IND and OOD noise remains largely unexplored, particularly when dealing with non-IID graph data. To address these challenges, this paper introduces a novel complex-noise-resistant open-set node classification approach, designed for open-set graph data containing both IND and OOD noisy nodes. |
Qin Zhang; Jiexin Lu; Xiaowei Li; Huisi Wu; Shirui Pan; Junyang Chen; |
| 198 | Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous camera-based methods struggle to predict accurate semantic scenes due to inherent geometric ambiguity and incomplete observations. In this paper, we resort to stereo matching technique and bird’s-eye-view (BEV) representation learning to address such issues in SSC. |
Bohan Li; Yasheng Sun; Zhujin Liang; Dalong Du; Zhuanghui Zhang; Xiaofeng Wang; Yunnan Wang; Xin Jin; Wenjun Zeng; |
| 199 | Fusion from A Distributional Perspective: A Unified Symbiotic Diffusion Framework for Any Multisource Remote Sensing Data Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a unified and self-supervised Symbiotic Diffusion framework (named SymDiffuser), which achieves the joint classification of any pair of different remote sensing data sources in a single model. |
Teng Yang; Song Xiao; Wenqian Dong; Jiahui Qu; Yueguang Yang; |
| 200 | Towards Generalizable Neural Solvers for Vehicle Routing Problems Via Ensemble with Transferrable Local Policy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To make neural VRP solvers more practical, we design an auxiliary policy that learns from the local transferable topological features, named local policy, and integrate it with a typical construction policy (which learns from the global information of VRP instances) to form an ensemble policy. |
Chengrui Gao; Haopu Shang; Ke Xue; Dong Li; Chao Qian; |
| 201 | A New Paradigm for Counterfactual Reasoning in Fairness and Recourse Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An inherent limitation of this paradigm is that some demographic interventions — like interventions on race — may not be well-defined or translate into the formalisms of interventional counterfactuals. In this work, we explore a new paradigm based instead on the backtracking counterfactual, where rather than imagine hypothetical interventions on legally-protected characteristics, we imagine alternate initial conditions while holding these characteristics fixed. |
Lucius E.J. Bynum; Joshua R. Loftus; Julia Stoyanovich; |
| 202 | Efficient Tuning and Inference for Large Language Models on Textual Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose ENGINE, a parameter- and memory-efficient fine-tuning method for textual graphs with an LLM encoder. |
Yun Zhu; Yaoke Wang; Haizhou Shi; Siliang Tang; |
| 203 | Hierarchical Decompositions and Termination Analysis for Generalized Planning (Abstract Reprint) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. |
Siddharth Srivastava; |
| 204 | Gradformer: Graph Transformer with Exponential Decay Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although some methods utilize positional encoding and attention bias to model inductive biases, their effectiveness is still suboptimal analytically. Therefore, this paper presents Gradformer, a method innovatively integrating GT with the intrinsic inductive bias by applying an exponential decay mask to the attention matrix. |
Chuang Liu; Zelin Yao; Yibing Zhan; Xueqi Ma; Shirui Pan; Wenbin Hu; |
| 205 | Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the potential of leveraging the graph’s structural composition as a fundamental and unique prior in the masked pre-training process. |
Chuang Liu; Yuyao Wang; Yibing Zhan; Xueqi Ma; Dapeng Tao; Jia Wu; Wenbin Hu; |
| 206 | Improving Multi-agent Reinforcement Learning with Stable Prefix Policy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We scale our approach to various value-based MARL methods and empirically verify our method in a cooperative MARL task, SMAC benchmarks. |
Yue Deng; Zirui Wang; Yin Zhang; |
| 207 | Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text Against Neural Machine Translation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel vision-fused attack (VFA) framework to acquire powerful adversarial text, i.e., more aggressive and stealthy. |
Yanni Xue; Haojie Hao; Jiakai Wang; Qiang Sheng; Renshuai Tao; Yu Liang; Pu Feng; Xianglong Liu; |
| 208 | Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Vermouth, a simple yet effective framework comprising a pre-trained Stable Diffusion (SD) model containing rich generative priors, a unified head (U-head) capable of integrating hierarchical representations, and an Adapted-Expert providing discriminative priors. |
Shiyin Dong; Mingrui Zhu; Kun Cheng; Nannan Wang; Xinbo Gao; |
| 209 | Approximate Algorithms for K-Sparse Wasserstein Barycenter with Outliers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the k-sparse WB problem in the presence of outliers, which is a more practical setting since real-world data often contains noise. |
Qingyuan Yang; Hu Ding; |
| 210 | Boosting Single Positive Multi-label Classification with Generalized Robust Loss Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate Single Positive Multi-label Learning (SPML), where each image is associated with merely one positive label. |
Yanxi Chen; Chunxiao Li; Xinyang Dai; Jinhuan Li; Weiyu Sun; Yiming Wang; Renyuan Zhang; Tinghe Zhang; Bo Wang; |
| 211 | MARS: Multimodal Active Robotic Sensing for Articulated Characterization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent studies mainly focus on point cloud, a single-modal approach, often neglecting vital texture and lighting details and assuming ideal conditions like optimal viewpoints, unrepresentative of real-world scenarios. To address these limitations, we introduce MARS, a novel framework for articulated object characterization. |
Hongliang Zeng; Ping Zhang; Chengjiong Wu; Jiahua Wang; Tingyu Ye; Fang Li; |
| 212 | SpecAR-Net: Spectrogram Analysis and Representation Network for Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present Spectrogram Analysis and Representation Network (SpecAR-Net). |
Yi Dong; Liwen Zhang; Youcheng Zhang; Shi Peng; Wen Chen; Zhe Ma; |
| 213 | Beyond Alignment: Blind Video Face Restoration Via Parsing-Guided Temporal-Coherent Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Restoring each frame independently in a naive manner inevitably introduces temporal incoherence and artifacts from pose changes and keypoint localization errors. To address this, we propose the first blind video face restoration approach with a novel parsing-guided temporal-coherent transformer (PGTFormer) without pre-alignment. |
Kepeng Xu; Li Xu; Gang He; Wenxin Yu; Yunsong Li; |
| 214 | Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide a systematical generalization analysis of the first-order gradient-based bilevel optimization methods. |
Xuelin Zhang; Hong Chen; Bin Gu; Tieliang Gong; Feng Zheng; |
| 215 | MOSER: Learning Sensory Policy for Task-specific Viewpoint Via View-conditional World Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent studies learn from different viewpoints with multiple fixed cameras, but this incurs high computation and storage costs and may not guarantee the coverage of the optimal viewpoint. To alleviate these limitations, we propose a straightforward View-conditional Partially Observable Markov Decision Processes (VPOMDPs) assumption and develop a new method, the MOdel-based SEnsor controlleR (MOSER). |
Shenghua Wan; Hai-Hang Sun; Le Gan; De-Chuan Zhan; |
| 216 | PiShield: A PyTorch Package for Learning with Requirements Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks’ topology. |
Mihaela C. Stoian; Alex Tatomir; Thomas Lukasiewicz; Eleonora Giunchiglia; |
| 217 | A Survey of Graph Meets Large Language Model: Progress and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. … |
Yuhan Li; Zhixun Li; Peisong Wang; Jia Li; Xiangguo Sun; Hong Cheng; Jeffrey Xu Yu; |
| 218 | Information-Theoretic Opacity-Enforcement in Markov Decision Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose novel algorithms to compute the policy gradient of entropy for each observation, leveraging message passing within the hidden Markov models. |
Chongyang Shi; Yuheng Bu; Jie Fu; |
| 219 | Learning A Spiking Neural Network for Efficient Image Deraining Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present an Efficient Spiking Deraining Network, called ESDNet. |
Tianyu Song; Guiyue Jin; Pengpeng Li; Kui Jiang; Xiang Chen; Jiyu Jin; |
| 220 | Protecting Split Learning By Potential Energy Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the privacy leakage from the forward embeddings of split learning. |
Fei Zheng; Chaochao Chen; Lingjuan Lyu; Xinyi Fu; Xing Fu; Weiqiang Wang; Xiaolin Zheng; Jianwei Yin; |
| 221 | FreqFormer: Frequency-aware Transformer for Lightweight Image Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the significant progress in SR, Transformer-based SR methods (e.g., SwinIR) still suffer from the problems of heavy computation cost and low-frequency preference, while ignoring the reconstruction of rich high-frequency information, hence hindering the representational power of Transformers. To address these issues, in this paper, we propose a novel Frequency-aware Transformer (FreqFormer) for lightweight image SR. |
Tao Dai; Jianping Wang; Hang Guo; Jinmin Li; Jinbao Wang; Zexuan Zhu; |
| 222 | Implicit Prompt Learning for Image Denoising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, various deep denoising methods have been proposed to solve the insufficient feature problem in image denoising. |
Yao Lu; Bo Jiang; Guangming Lu; Bob Zhang; |
| 223 | CompetEvo: Towards Morphological Evolution from Competition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, whether there exists an optimal configuration and tactics for agents in a multiagent competition scenario is still an issue that is challenging to definitively conclude. In this context, we propose competitive evolution (CompetEvo), which co-evolves agents’ designs and tactics in confrontation. |
Kangyao Huang; Di Guo; Xinyu Zhang; Xiangyang Ji; Huaping Liu; |
| 224 | Improving Paratope and Epitope Prediction By Multi-Modal Contrastive Learning and Interaction Informativeness Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure data of antibodies and antigens. |
Zhiwei Wang; Yongkang Wang; Wen Zhang; |
| 225 | SCAT: A Time Series Forecasting with Spectral Central Alternating Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing models based on transformer architecture are limited to classical design, ignoring the impact of spatial information and noise on model architecture design. Therefore, we propose an atypical design of Transformer-based models for multivariate time series forecasting. |
Chengjie Zhou; Chao Che; Pengfei Wang; Qiang Zhang; |
| 226 | Self-Repellent Random Walks on General Graphs – Achieving Minimal Sampling Variance Via Nonlinear Markov Chains (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. |
Vishwaraj Doshi; Jie Hu; Do Young Eun; |
| 227 | Diffutoon: High-Resolution Editable Toon Shading Via Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we model the toon shading problem as four subproblems, i.e., stylization, consistency enhancement, structure guidance, and colorization. |
Zhongjie Duan; Chengyu Wang; Cen Chen; Weining Qian; Jun Huang; |
| 228 | Dynamic Brightness Adaptation for Robust Multi-modal Image Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing fusion methods lack robustness against such brightness perturbations, significantly compromising the visual fidelity of the fused imagery. To address this challenge, we propose the Brightness Adaptive multimodal dynamic fusion framework (BA-Fusion), which achieves robust image fusion despite dynamic brightness fluctuations. |
Yiming Sun; Bing Cao; Pengfei Zhu; Qinghua Hu; |
| 229 | MusicMagus: Zero-Shot Text-to-Music Editing Via Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the task of editing these generated music remains a significant challenge. This paper introduces a novel approach to edit music generated by such models, enabling the modification of specific attributes, such as genre, mood, and instrument, while maintaining other aspects unchanged. |
Yixiao Zhang; Yukara Ikemiya; Gus Xia; Naoki Murata; Marco A. Martínez-Ramírez; Wei-Hsiang Liao; Yuki Mitsufuji; Simon Dixon; |
| 230 | Building Expressive and Tractable Probabilistic Generative Models: A Review Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a comprehensive survey of the advancements and techniques in the field of tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits (PCs). |
Sahil Sidheekh; Sriraam Natarajan; |
| 231 | Expanding The Reach of Social Choice Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, I present an overview of my efforts to expand the reach of social choice theory in the domains of fair division, voting, and tournaments. |
Warut Suksompong; |
| 232 | OSIC: A New One-Stage Image Captioner Coined Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel One-Stage Image Captioner (OSIC) with dynamic multi-sight learning, which directly transforms the images into descriptive sentences in one stage for eliminating the information gap. |
Bo Wang; Zhao Zhang; Mingbo Zhao; Xiaojie Jin; Mingliang Xu; Meng Wang; |
| 233 | A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce an additional instructor model to evaluate the accuracy and trustworthiness of proxy labels because existing pseudo-labeling approaches require probabilistic outputs to reveal the model’s confidence and fail to be applied in regression tasks. |
Fang Wu; |
| 234 | Contrastive and View-Interaction Structure Learning for Multi-view Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, existing contrastive loss imports numerous false negative pairs that conflict with the clustering objectives. In response to these challenges, we propose a contraStive and viEw-interaction stRucture learning framework for multI-viEw cluStering (SERIES). |
Jing Wang; Songhe Feng; |
| 235 | Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing Via Content-based Controls Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tasks. To bridge this gap, To address this gap, we propose a novel approach leveraging a parameter-efficient heterogeneous adapter combined with a masking training scheme. |
Liwei Lin; Gus Xia; Yixiao Zhang; Junyan Jiang; |
| 236 | Empirical Analysis of Dialogue Relation Extraction with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Interestingly, we discover that LLMs significantly alleviate two issues in existing DRE methods. |
Guozheng Li; Zijie Xu; Ziyu Shang; Jiajun Liu; Ke Ji; Yikai Guo; |
| 237 | An Archive Can Bring Provable Speed-ups in Multi-Objective Evolutionary Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we theoretically show, for the first time, that using an archive can guarantee speed-ups for MOEAs. |
Chao Bian; Shengjie Ren; Miqing Li; Chao Qian; |
| 238 | Dual Contrastive Graph-Level Clustering with Multiple Cluster Perspectives Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods generally rely on a single clustering criterion, e.g., $k$-means, which limits their abilities to fully exploit the complex Euclidean and structural information inherent in graphs. To bridge this gap, we propose a dual contrastive graph-level clustering (DCGLC) method in this paper. |
Jinyu Cai; Yunhe Zhang; Jicong Fan; Yali Du; Wenzhong Guo; |
| 239 | A Complete Landscape of EFX Allocations on Graphs: Goods, Chores and Mixed Manna Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study envy-free up to any item (EFX) allocations on graphs where vertices and edges represent agents and items respectively. |
Yu Zhou; Tianze Wei; Minming Li; Bo Li; |
| 240 | Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an algorithm named ELIMIPL, i.e., Exploiting conjugate Label Information for Multi-Instance Partial-Label learning, which exploits the conjugate label information to improve the disambiguation performance. |
Wei Tang; Weijia Zhang; Min-Ling Zhang; |
| 241 | Self-supervised Weighted Information Bottleneck for Multi-view Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel self-supervised weighted information bottleneck (SWIB) method for solving the multi-view clustering problem. |
Zhengzheng Lou; Chaoyang Zhang; Hang Xue; Yangdong Ye; Qinglei Zhou; Shizhe Hu; |
| 242 | Navigating Continual Test-time Adaptation with Symbiosis Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The long-term accumulation of these negative effects exacerbates the model’s difficulty in generalizing to future domain shifts and contributes to catastrophic forgetting. To address these challenges, this paper introduces a Dual-stream Network that independently optimizes different parameters in each stream to capture symbiotic knowledge from continual domains, thereby ensuring generalization while enhancing instantaneous discrimination. |
Xu Yang; Moqi Li; Jie Yin; Kun Wei; Cheng Deng; |
| 243 | FedPFT: Federated Proxy Fine-Tuning of Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Feder- ated Proxy Fine-Tuning (FedPFT), a novel method enhancing FMs adaptation in downstream tasks through FL by two key modules. |
Zhaopeng Peng; Xiaoliang Fan; Yufan Chen; Zheng Wang; Shirui Pan; Chenglu Wen; Ruisheng Zhang; Cheng Wang; |
| 244 | Learning Hierarchy-Enhanced POI Category Representations Using Disentangled Mobility Sequences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such an approach does not fully capture the underlying hierarchical relationship between POI categories and can hardly integrate the category hierarchy into various deep sequential models. To overcome this shortcoming, we propose a Semantically Disentangled POI Category Embedding Model (SD-CEM) to generate hierarchy-enhanced category representations using disentangled mobility sequences. |
Hongwei Jia; Meng Chen; Weiming Huang; Kai Zhao; Yongshun Gong; |
| 245 | CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, fine-tuning on smaller datasets risks overfitting. To address these issues, we propose Coarse-to-Fine Instruction Alignment (CoFInAl). |
Kanglei Zhou; Junlin Li; Ruizhi Cai; Liyuan Wang; Xingxing Zhang; Xiaohui Liang; |
| 246 | PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, we advocate for the customization of global information tailored to each agent, creating agent-personalized global information to bolster overall performance. |
Yiqun Chen; Hangyu Mao; Jiaxin Mao; Shiguang Wu; Tianle Zhang; Bin Zhang; Wei Yang; Hongxing Chang; |
| 247 | Natural Language-centered Inference Network for Multi-modal Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Natural Language-centered Inference Network (NLIN) for multi-modal fake news detection by aligning multi-modal news content with the natural language space and introducing an encoder-decoder architecture to fully comprehend the news in-context. |
Qiang Zhang; Jiawei Liu; Fanrui Zhang; Jingyi Xie; Zheng-Jun Zha; |
| 248 | Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While achieving promising results, current traffic speed prediction methods still suffer from ignoring topology-free patterns, which cannot be captured by GNNs. To tackle this challenge, we propose a generic model for enabling the current GNN-based methods to preserve topology-free patterns. |
Yicheng Zhou; Pengfei Wang; Hao Dong; Denghui Zhang; Dingqi Yang; Yanjie Fu; Pengyang Wang; |
| 249 | Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint) Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have … |
Yang Cao; Ye Zhu; Kai Ming Ting; Flora D. Salim; Hong Xian Li; Luxing Yang; Gang Li; |
| 250 | LLM-based Multi-Level Knowledge Generation for Few-shot Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Knowledge Graphs (KGs) are pivotal in various NLP applications but often grapple with incompleteness, especially due to the long-tail problem where infrequent, unpopular relationships drastically reduce the KG completion performance. In this paper, we focus on Few-shot Knowledge Graph Completion (FKGC), a task addressing these gaps in long-tail scenarios. |
Qian Li; Zhuo Chen; Cheng Ji; Shiqi Jiang; Jianxin Li; |
| 251 | Boosting Diffusion Models with An Adaptive Momentum Sampler Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The sampling process of DPMs is mathematically similar to Stochastic Gradient Descent (SGD), with both being iteratively updated with a function increment. Building on this, we present a novel reverse sampler for DPMs in this paper, drawing inspiration from the widely-used Adam optimizer. |
Xiyu Wang; Anh-Dung Dinh; Daochang Liu; Chang Xu; |
| 252 | Pre-DyGAE: Pre-training Enhanced Dynamic Graph Autoencoder for Occupational Skill Demand Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Pre-training Enhanced Dynamic Graph Autoencoder (Pre-DyGAE), forecasting skill demand from an occupational perspective. |
Xi Chen; Chuan Qin; Zhigaoyuan Wang; Yihang Cheng; Chao Wang; Hengshu Zhu; Hui Xiong; |
| 253 | Nonconvex Multiview Subspace Clustering Framework with Efficient Method Designs and Theoretical Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing MvSC methods still have two shortcomings: (1) they adopt the nuclear norm as the low-rank constraint, which makes it impossible to fully exploit the mutually complementary subspace information, and (2) they do not handle disjoint and confounding points carefully, which may degrade the purity and distinctiveness of cross-view fusion. To address these issues, in this paper we propose a novel MvSC model with nonconvex ℓq regularization. |
Zhi Wang; Zhuo Liu; Dong Hu; Tao Jia; |
| 254 | ADELT: Transpilation Between Deep Learning Frameworks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the Adversarial DEep Learning Transpiler (ADELT), a novel approach to source-to-source transpilation between deep learning frameworks. |
Linyuan Gong; Jiayi Wang; Alvin Cheung; |
| 255 | WPML3CP: Wasserstein Partial Multi-Label Learning with Dual Label Correlation Perspectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The two techniques are coupled to promote precise estimations of label correlations. Upon these ideas, we propose a novel PMLL method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (WPML3CP). |
Ximing Li; Yuanchao Dai; Bing Wang; Changchun Li; Renchu Guan; Fangming Gu; Jihong Ouyang; |
| 256 | ReportParse: A Unified NLP Tool for Extracting Document Structure and Semantics of Corporate Sustainability Reporting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ReportParse, a Python-based tool designed to parse corporate sustainability reports.It combines document structure analysis with natural language processing (NLP) models to extract sustainability-related information from the reports.We also provide easy-to-use web and command interfaces.The tool is expected to aid researchers and analysts in evaluating corporate commitment to and management of sustainability efforts. |
Gaku Morio; Soh Young In; Jungah Yoon; Harri Rowlands; Christopher Manning; |
| 257 | A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation. |
Mohammad Hashemi; Shengbo Gong; Juntong Ni; Wenqi Fan; B. Aditya Prakash; Wei Jin; |
| 258 | Implicit Anomaly Subgraph Detection (IASD) in Multi-Domain Attribute Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Anomaly subgraph detection is a vital task in various real applications. However, with the advancement of AI technology, it faces new challenges: 1) Anomaly features are often … |
Ying Sun; |
| 259 | An NCDE-based Framework for Universal Representation Learning of Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, time series exhibit diverse distributions and inherent characteristics, particularly with the common occurrence of missing values, posing a notable challenge for existing backbones in effectively handling such diverse time series data. To bridge these gaps, we propose CTRL, a framework for universal TSRL. |
Zihan Liu; Bowen Du; Junchen Ye; Xianqing Wen; Leilei Sun; |
| 260 | Multimodal Representation Distribution Learning for Medical Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of existing methods is limited by the lack of high-quality labeled data due to the expensive data annotation. To alleviate this limitation, we propose a novel multi-modal learning method for medical image segmentation. |
Chao Huang; Weichao Cai; Qiuping Jiang; Zhihua Wang; |
| 261 | LG-GNN: Local-Global Adaptive Graph Neural Network for Modeling Both Homophily and Heterophily Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in the aggregation process of GNNs, the difference in modeling global and local information is not considered, inevitably leading to information loss. Motivated by this limitation, we propose LG-GNN, a local-global adaptive graph neural network for modeling both homophily and heterophily. |
Zhizhi Yu; Bin Feng; Dongxiao He; Zizhen Wang; Yuxiao Huang; Zhiyong Feng; |
| 262 | From Skepticism to Acceptance: Simulating The Attitude Dynamics Toward Fake News Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, in this work, we introduce a Fake news Propagation Simulation framework (FPS) based on LLM, which studies the trends and control of fake news propagation in detail. |
Yuhan Liu; Xiuying Chen; Xiaoqing Zhang; Xing Gao; Ji Zhang; Rui Yan; |
| 263 | Towards Highly Realistic Artistic Style Transfer Via Stable Diffusion with Step-aware and Layer-aware Prompt Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, diffusion model-based methods generally fail to preserve the content structure of input content images well, introducing some undesired content structure and style patterns. To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns. |
Zhanjie Zhang; Quanwei Zhang; Huaizhong Lin; Wei Xing; Juncheng Mo; Shuaicheng Huang; Jinheng Xie; Guangyuan Li; Junsheng Luan; Lei Zhao; Dalong Zhang; Lixia Chen; |
| 264 | Partial Optimal Transport Based Out-of-Distribution Detection for Open-Set Semi-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods often struggle to provide effective OOD detection strategies, especially when dealing with datasets comprising a large number of training categories. In response to this challenge, we model the OOD detection problem in OSSL as a partial optimal transport (POT) problem. |
Yilong Ren; Chuanwen Feng; Xike Xie; S. Kevin Zhou; |
| 265 | MultifacetEval: Multifaceted Evaluation to Probe LLMs in Mastering Medical Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, there still exists a significant gap between the reported performance and the practical effectiveness in real-world medical scenarios. In this paper, we aim to explore the causes of this gap by employing a multifaceted examination schema to systematically probe the actual mastery of medical knowledge by current LLMs. |
Yuxuan Zhou; Xien Liu; Chen Ning; Ji Wu; |
| 266 | Machine Unlearning: Challenges in Data Quality and Access Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: During the unlearning process, models are typically presented with data that specifies which information should be erased and which should be retained. |
Miao Xu; |
| 267 | DTS-TPT: Dual Temporal-Sync Test-time Prompt Tuning for Zero-shot Activity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this work proposes a Dual Temporal-Sync Test-time Prompt Tuning (DTS-TPT) framework for zero-shot activity recognition. |
Rui Yan; Hongyu Qu; Xiangbo Shu; Wenbin Li; Jinhui Tang; Tieniu Tan; |
| 268 | Self-Supervised Vision for Climate Downscaling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a self-supervised deep learning model that does not require high resolution ground truth data for downscaling. |
Karandeep Singh; Chaeyoon Jeong; Naufal Shidqi; Sungwon Park; Arjun Nellikkattil; Elke Zeller; Meeyoung Cha; |
| 269 | Zero-shot Learning for Preclinical Drug Screening Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. |
Kun Li; Weiwei Liu; Yong Luo; Xiantao Cai; Jia Wu; Wenbin Hu; |
| 270 | Contrastive Learning Drug Response Models from Natural Language Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose CLDR, a contrastive learning framework with natural language supervision for the DRP. |
Kun Li; Xiuwen Gong; Jia Wu; Wenbin Hu; |
| 271 | SAEIR: Sequentially Accumulated Entropy Intrinsic Reward for Cooperative Multi-Agent Reinforcement Learning with Sparse Reward Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are usually sparse reward settings in many real-world multi-agent systems, which makes it difficult for MARL algorithms to successfully learn an effective strategy. To tackle this problem, we propose a novel sequentially accumulated entropy intrinsic reward named SAEIR, which utilizes the entropy of multi-agent system as a bonus to accelerate learning. |
Xin He; Hongwei Ge; Yaqing Hou; Jincheng Yu; |
| 272 | Population-Based Diverse Exploration for Sparse-Reward Multi-Agent Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that with a suitable exploration method, maintaining a population of joint policies rather than one joint policy can significantly improve exploration. |
Pei Xu; Junge Zhang; Kaiqi Huang; |
| 273 | Generalized Taxonomy-Guided Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The existing approaches for learning implicit hierarchical network structures focus on introducing taxonomy to graph neural networks but often run short of exploiting the rich network semantics and structural properties in the taxonomy, resulting in poor generalizability and reusability. To address these issues, we propose generalized Taxonomy-Guided Graph Neural Networks (TG-GNN) to integrate taxonomy into network representation learning. |
Yu Zhou; Di Jin; Jianguo Wei; Dongxiao He; Zhizhi Yu; Weixiong Zhang; |
| 274 | Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the real world, there exist a class of optimization problems that multiple (local) optimal solutions in the solution space correspond to a single point in the objective space. In this paper, we theoretically show that for such multimodal problems, a simple method that considers the diversity of solutions in the solution space can benefit the search in evolutionary algorithms (EAs). |
Shengjie Ren; Zhijia Qiu; Chao Bian; Miqing Li; Chao Qian; |
| 275 | Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning Via Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Internet of Things (IoT) devices possess valuable yet private multimodal data, calling for a decentralized machine learning scheme. Though several multimodal federated learning (MFL) methods have been proposed, most of them merely overlook the system heterogeneity across IoT devices, resulting in the inadaptability to real world applications. |
Jinqian Chen; Haoyu Tang; Junhao Cheng; Ming Yan; Ji Zhang; Mingzhu Xu; Yupeng Hu; Liqiang Nie; |
| 276 | Safeguarding Sustainable Cities: Unsupervised Video Anomaly Detection Through Diffusion-based Latent Pattern Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, a diffusion-based latent pattern learning UVAD framework is proposed, called DiffVAD. |
Menghao Zhang; Jingyu Wang; Qi Qi; Pengfei Ren; Haifeng Sun; Zirui Zhuang; Lei Zhang; Jianxin Liao; |
| 277 | Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. |
Yang Wang; Haiyang Mei; Qirui Bao; Ziqi Wei; Mike Zheng Shou; Haizhou Li; Bo Dong; Xin Yang; |
| 278 | A Meta-Game Evaluation Framework for Deep Multiagent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a meta-game evaluation framework for deep MARL, by framing each MARL algorithm as a meta-strategy, and repeatedly sampling normal-form empirical games over combinations of meta-strategies resulting from different random seeds. |
Zun Li; Michael P. Wellman; |
| 279 | Federated Adaptation for Foundation Model-based Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel federated adaptation mechanism to enhance the foundation model-based recommendation system in a privacy-preserving manner. |
Chunxu Zhang; Guodong Long; Hongkuan Guo; Xiao Fang; Yang Song; Zhaojie Liu; Guorui Zhou; Zijian Zhang; Yang Liu; Bo Yang; |
| 280 | Towards Dynamic-Prompting Collaboration for Source-Free Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Without the need for accessing source data, our method amalgamates the strengths inherent in both traditional SFDA approaches and vision-language models, formulating a collaborative framework for addressing SFDA challenges. |
Mengmeng Zhan; Zongqian Wu; Rongyao Hu; Ping Hu; Heng Tao Shen; Xiaofeng Zhu; |
| 281 | Exploring The Role of Node Diversity in Directed Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Many methods of Directed Graph Neural Networks (DGNNs) are designed to equally treat nodes in the same neighbor set (i.e., out-neighbor set and in-neighbor set) for every node, … |
Jincheng Huang; Yujie Mo; Ping Hu; Xiaoshuang Shi; Shangbo Yuan; Zeyu Zhang; Xiaofeng Zhu; |
| 282 | Cross-View Diversity Embedded Consensus Learning for Multi-View Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel MVC method, named CCL-MVC. |
Chong Peng; Kai Zhang; Yongyong Chen; Chenglizhao Chen; Qiang Cheng; |
| 283 | Individual Causal Structure Learning from Population Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we propose an Individual Linear Acyclic Model (ILAM) for each individual from population data, which models the individual’s variables as being linearly influenced by their parents, in addition to environment variables and noise terms. |
Wei Chen; Xiaokai Huang; Zijian Li; Ruichu Cai; Zhiyi Huang; Zhifeng Hao; |
| 284 | When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers Via Membership Inference Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While in-processing fairness approaches show promise in mitigating bias predictions, their potential impact on privacy leakage remains under-explored. We aim to address this gap by assessing the privacy risks of fairness-enhanced binary classifiers with membership inference attacks (MIAs). |
Huan Tian; Guangsheng Zhang; Bo Liu; Tianqing Zhu; Ming Ding; Wanlei Zhou; |
| 285 | Deciphering The Projection Head: Representation Evaluation Self-supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we systematically investigate the role of the projection head in SSL. |
Jiajun Ma; Tianyang Hu; Wenjia Wang; |
| 286 | Efficiency Calibration of Implicit Regularization in Deep Networks Via Self-paced Curriculum-Driven Singular Value Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new explicit regularization method that calibrates the implicit bias towards low-rank trends in matrix completion tasks. |
Zhe Li; Shuo Chen; Jian Yang; Lei Luo; |
| 287 | Budget Feasible Mechanisms: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we provide a comprehensive overview of key procurement auction settings and results of budget feasible mechanisms. |
Xiang Liu; Hau Chan; Minming Li; Weiwei Wu; |
| 288 | MuChin: A Chinese Colloquial Description Benchmark for Evaluating Language Models in The Field of Music Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present MuChin, the first open-source music description benchmark in Chinese colloquial language, designed to evaluate the performance of multimodal LLMs in understanding and describing music. |
Zihao Wang; Shuyu Li; Tao Zhang; Qi Wang; Pengfei Yu; Jinyang Luo; Yan Liu; Ming Xi; Kejun Zhang; |
| 289 | ParaILP: A Parallel Local Search Framework for Integer Linear Programming with Cooperative Evolution Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the first parallel local search framework (ParaILP) for solving the general ILP problem, based on two novel ideas: a new initialization method named polarity initialization to construct different initial solutions for local search threads and a cooperative evolution mechanism for managing and generating high-quality solutions using information shared by different threads. |
Peng Lin; Mengchuan Zou; Zhihan Chen; Shaowei Cai; |
| 290 | EC-SNN: Splitting Deep Spiking Neural Networks for Edge Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a deep SNN splitting framework named EC-SNN to run the intricate SNN models on edge devices. |
Di Yu; Xin Du; Linshan Jiang; Wentao Tong; Shuiguang Deng; |
| 291 | Error-aware Sampling in Adaptive Shells for Neural Surface Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead, we introduce an error-aware sampling method based on thin intervals of valid weight distributions, dubbed adaptive shells, to reduce the number of samples while still maintaining the reconstruction accuracy. |
Qi Wang; Yuchi Huo; Qi Ye; Rui Wang; Hujun Bao; |
| 292 | What Is Best for Students, Numerical Scores or Letter Grades? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Typically, a numerical score obtained via one or more evaluations is converted into a letter grade (e.g., A+, B-, etc.) by associating a disjoint interval of numerical scores to each letter grade. We propose the first model for studying the (de)motivational effects of such grading on the students and, consequently, on their performance in future evaluations. |
Evi Micha; Shreyas Sekar; Nisarg Shah; |
| 293 | Enhancing Fine-Grained Urban Flow Inference Via Incremental Neural Operator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by recent operator learning, we present an Urban Neural Operator solution with Incremental learning (UNOI), primarily seeking to learn grained-invariant solutions for FUFI in addition to addressing CF. Specifically, we devise an urban neural operator (UNO) in UNOI that learns mappings between approximation spaces by treating the different-grained flows as continuous functions, allowing a more flexible capture of spatial correlations. |
Qiang Gao; Xiaolong Song; Li Huang; Goce Trajcevski; Fan Zhou; Xueqin Chen; |
| 294 | FineFMPL: Fine-grained Feature Mining Prompt Learning for Few-Shot Class Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Benefiting from the powerful visual and textual understanding ability of Vision-Language (VL) pre-training models, we propose a Fine-grained Feature Mining Prompt Learning (FineFMPL) approach to adapt the VL model to FSCIL, which comprehensively learns and memorizes fine-grained discriminative information of emerging classes. |
Hongbo Sun; Jiahuan Zhou; Xiangteng He; Jinglin Xu; Yuxin Peng; |
| 295 | A Density-driven Iterative Prototype Optimization for Transductive Few-shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Few-shot learning (FSL) poses a considerable challenge since it aims to improve the model generalization ability with limited labeled data. Previous works usually attempt to … |
Jingcong Li; Chunjin Ye; Fei Wang; Jiahui Pan; |
| 296 | FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the problem with dual skewed non-IID data, in this paper, we propose a federated learning algorithm based on local graph, named FBLG. |
Yi Xu; Ying Li; Haoyu Luo; Xiaoliang Fan; Xiao Liu; |
| 297 | Separate in The Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance. To tackle this issue, we propose AVSepChain, drawing inspiration from the speech chain concept. |
Zhaoxi Mu; Xinyu Yang; |
| 298 | MLP-DINO: Category Modeling and Query Graphing with Deep MLP for Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This oversight leads to an imbalanced spatial distribution of queries (SDQ). In this paper, we propose a new MLP-DINO model to address these issues. |
Guiping Cao; Wenjian Huang; Xiangyuan Lan; Jianguo Zhang; Dongmei Jiang; Yaowei Wang; |
| 299 | AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a comprehensive examination of the burgeoning field of AI-enhanced VR applications in medical care and services. |
Yixuan Wu; Kaiyuan Hu; Danny Z. Chen; Jian Wu; |
| 300 | Intelligent Agents for Auction-based Federated Learning: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper bridges this important gap by providing a first-of-its-kind survey on the Intelligent Agents for AFL (IA-AFL) literature. We propose a unique multi-tiered taxonomy that organises existing IA-AFL works according to 1) the stakeholders served, 2) the auction mechanism adopted, and 3) the goals of the agents, to provide readers with a multi-perspective view into this field. |
Xiaoli Tang; Han Yu; Xiaoxiao Li; Sarit Kraus; |
| 301 | Subgraph Pooling: Tackling Negative Transfer on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we identify that structural differences significantly amplify the dissimilarities in the node embeddings across graphs. To mitigate this, we bring a new insight in this paper: for semantically similar graphs, although structural differences lead to significant distribution shift in node embeddings, their impact on subgraph embeddings could be marginal. |
Zehong Wang; Zheyuan Zhang; Chuxu Zhang; Yanfang Ye; |
| 302 | Prompt Learning for Generalized Vehicle Routing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To be concrete, we propose a novel prompt learning method to facilitate fast zero-shot adaptation of a pre-trained model to solve routing problem instances from different distributions. |
Fei Liu; Xi Lin; Weiduo Liao; Zhenkun Wang; Qingfu Zhang; Xialiang Tong; Mingxuan Yuan; |
| 303 | G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the recursive strategy suffers from the accumulated error, while the simultaneous strategy overlooks the constraints among future steps, resulting in kinematically infeasible predictions. To address these issues, in this paper, we propose G2LTraj, a plug-and-play global-to-local generation approach for trajectory prediction. |
Zhanwei Zhang; Zishuo Hua; Minghao Chen; Wei Lu; Binbin Lin; Deng Cai; Wenxiao Wang; |
| 304 | Prompt Learning with Extended Kalman Filter for Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an abstraction of the prompt learning process using an extended Kalman filter. |
Quan Li; Xike Xie; Chao Wang; S. Kevin Zhou; |
| 305 | Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, considering the crucial role of the body parts in the spatially concentrated human actions, we attend to the mixing augmentations and propose a novel method, Shap-Mix, which improves long-tailed learning by mining representative motion patterns for tail categories. |
Jiahang Zhang; Lilang Lin; Jiaying Liu; |
| 306 | Empowering Time Series Analysis with Large Language Models: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we provide a systematic overview of existing methods that leverage LLMs for time series analysis. |
Yushan Jiang; Zijie Pan; Xikun Zhang; Sahil Garg; Anderson Schneider; Yuriy Nevmyvaka; Dongjin Song; |
| 307 | Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we tackle an annotation-free setting and propose a self-guided spurious correlation mitigation framework. |
Guangtao Zheng; Wenqian Ye; Aidong Zhang; |
| 308 | VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between variables. To fill this gap, we propose Variable Correlation Transformer (VCformer), which utilizes Variable Correlation Attention (VCA) module to mine the correlations among variables. |
Yingnan Yang; Qingling Zhu; Jianyong Chen; |
| 309 | O2ARC 3.0: A Platform for Solving and Creating ARC Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce O2ARC 3.0 interface for the Abstraction and Reasoning Corpus (ARC). |
Suyeon Shim; Dohyun Ko; Hosung Lee; Seokki Lee; Doyoon Song; Sanha Hwang; Sejin Kim; Sundong Kim; |
| 310 | CoCoG: Controllable Visual Stimuli Generation Based on Human Concept Representations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we present the Concept based Controllable Generation (CoCoG) framework. |
Chen Wei; Jiachen Zou; Dietmar Heinke; Quanying Liu; |
| 311 | Computing Optimal Equilibria in Repeated Games with Restarts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, a player can defect and then avoid penalization by immediately switching partners. In this paper, we focus on a specific set of equilibria that avoids this pitfall. |
Ratip Emin Berker; Vincent Conitzer; |
| 312 | A Survey of Constraint Formulations in Safe Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This challenge stems from the diversity of constraint representations and little exploration of their interrelations. To bridge this knowledge gap, we present a comprehensive review of representative constraint formulations, along with a curated selection of algorithms designed specifically for each formulation. |
Akifumi Wachi; Xun Shen; Yanan Sui; |
| 313 | Dual Enhancement in ODI Super-Resolution: Adapting Convolution and Upsampling to Projection Distortion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, the computational and memory costs associated with large-sized ODIs present a challenge for real-world application. To address these issues, we propose an efficient distortion-adaptive super-resolution network (ODA-SRN). |
Xiang Ji; Changqiao Xu; Lujie Zhong; Shujie Yang; Han Xiao; Gabriel-Miro Muntean; |
| 314 | Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose an innovative framework known as Disentanglement for Counterfactual Fairness-aware Domain Generalization (DCFDG). |
Yujie Lin; Chen Zhao; Minglai Shao; Baoluo Meng; Xujiang Zhao; Haifeng Chen; |
| 315 | ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite its benefits, existing offline-to-online RL methods suffer from performance degradation and slow improvement during the online phase. To tackle these challenges, we propose a novel framework called ENsemble-based Offline-To-Online (ENOTO) RL. |
Kai Zhao; Jianye Hao; Yi Ma; Jinyi Liu; Yan Zheng; Zhaopeng Meng; |
| 316 | Towards A Theory of Machine Learning on Graphs and Its Applications in Combinatorial Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we survey the author’s and his collaborators’ progress in developing a deeper theoretical understanding of GNNs’ expressive power and generalization abilities. |
Christopher Morris; |
| 317 | VSGT: Variational Spatial and Gaussian Temporal Graph Models for EEG-based Emotion Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, the cross temporal dependency between consecutive time slices for different brain regions is ignored. To address these limitations, in this paper, we propose Variational Spatial and Gaussian Temporal (VSGT) graph models to investigate the spatial and temporal dependencies for EEG-based emotion recognition. |
Chenyu Liu; Xinliang Zhou; Jiaping Xiao; Zhengri Zhu; Liming Zhai; Ziyu Jia; Yang Liu; |
| 318 | A Coarse-to-Fine Fusion Network for Event-Based Image Deblurring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a pioneering event-based coarse-to-fine image deblurring network named CFFNet. |
Huan Li; Hailong Shi; Xingyu Gao; |
| 319 | Predictive Accuracy-Based Active Learning for Medical Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we propose an efficient Predictive Accuracy-based Active Learning (PAAL) method for medical image segmentation, first introducing predictive accuracy to define uncertainty. |
Jun Shi; Shulan Ruan; Ziqi Zhu; Minfan Zhao; Hong An; Xudong Xue; Bing Yan; |
| 320 | TFWT: Tabular Feature Weighting with Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. |
Xinhao Zhang; Zaitian Wang; Lu Jiang; Wanfu Gao; Pengfei Wang; Kunpeng Liu; |
| 321 | Dynamic Many-Objective Molecular Optimization: Unfolding Complexity with Objective Decomposition and Progressive Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in real-world applications, the number of molecular objectives can be more than four (many-objective) and additional objectives may be introduced over time (dynamic-objective). To fill this gap, we propose DyMol, the first method designed to tackle the dynamic many-objective molecular optimization problem by utilizing a novel divide-and-conquer approach combined with a decomposition strategy. |
Dong-Hee Shin; Young-Han Son; Deok-Joong Lee; Ji-Wung Han; Tae-Eui Kam; |
| 322 | ADMN: Agent-Driven Modular Network for Dynamic Parameter Sharing in Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, applying parameter sharing among agents indiscriminately hinders the emergence of agents diversity and degrades the final cooperative performance. To better balance parameter sharing and agents diversity, we propose a novel Agent-Driven Modular Network (ADMN), where agents share a base network consisting of multiple specialized modules, and each agent has its own routing to connect these modules. |
Yang Yu; Qiyue Yin; Junge Zhang; Pei Xu; Kaiqi Huang; |
| 323 | A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Aiming at distilling a compact subgraph with condensed information, it facilitates various downstream KG-based tasks. In this survey paper, we are among the first to provide a systematic overview of its applications and define a taxonomy for existing methods from its interdisciplinary studies. |
Xiaxia Wang; Gong Cheng; |
| 324 | A Survey of Multimodal Sarcasm Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the first comprehensive survey on multimodal sarcasm detection – henceforth MSD – to date. |
Shafkat Farabi; Tharindu Ranasinghe; Diptesh Kanojia; Yu Kong; Marcos Zampieri; |
| 325 | Unsupervised Anomaly Detection Via Masked Diffusion Posterior Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models lack strict mathematical support for normal image reconstruction and unexpectedly suffer from low reconstruction quality. To address these issues, this paper proposes a novel and highly-interpretable method named Masked Diffusion Posterior Sampling (MDPS). |
Di Wu; Shicai Fan; Xue Zhou; Li Yu; Yuzhong Deng; Jianxiao Zou; Baihong Lin; |
| 326 | Robust Counterfactual Explanations in Machine Learning: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since a lack of robustness may compromise the validity of CEs, techniques to mitigate this risk are in order. In this survey, we review works in the rapidly growing area of robust CEs and perform an in-depth analysis of the forms of robustness they consider. |
Junqi Jiang; Francesco Leofante; Antonio Rago; Francesca Toni; |
| 327 | FD-UAD: Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We create FD-UAD, Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement. |
Yang Chang; Yuxuan Lin; Boyang Wang; Qing Zhao; Yan Wang; Wenqiang Zhang; |
| 328 | Pluggable Watermarking of Deepfake Models for Deepfake Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a pluggable and efficient active model watermarking framework for Deepfake detection. |
Han Bao; Xuhong Zhang; Qinying Wang; Kangming Liang; Zonghui Wang; Shouling Ji; Wenzhi Chen; |
| 329 | Let’s Start Over: Retraining with Selective Samples for Generalized Category Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel methodusing Nearest Neighbor Distance-aware Label Consistencysample selection. |
Zhimao Peng; Enguang Wang; Xialei Liu; Ming-Ming Cheng; |
| 330 | C3L: Content Correlated Vision-Language Instruction Tuning Data Generation Via Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new Content Correlated VLIT data generation via Contrastive Learning (C3L). |
Ji Ma; Wei Suo; Peng Wang; Yanning Zhang; |
| 331 | Large Language Model As A Policy Teacher for Training Reinforcement Learning Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: On the other hand, reinforcement learning (RL) approaches train agents that specialize in the target task but often suffer from low sampling efficiency and high exploration costs. In this paper, we introduce a novel framework that addresses these challenges by training a smaller, specialized student RL agent using instructions from an LLM-based teacher agent. |
Zihao Zhou; Bin Hu; Chenyang Zhao; Pu Zhang; Bin Liu; |
| 332 | Towards A Pretrained Model for Restless Bandits Via Multi-arm Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore several important question such as how to handle arms opting-in and opting-out over time without frequent retraining from scratch, how to deal with continuous state settings with nonlinear reward functions, which appear naturally in practical contexts. We address these questions by developing a pre-trained model (PreFeRMAB) based on a novel combination of three key ideas: (i) to enable fast generalization, we use train agents to learn from each other’s experience; (ii) to accommodate streaming RMABs, we derive a new update rule for a crucial $\lambda$-network; (iii) to handle more complex continuous state settings, we design the algorithm to automatically define an abstract state based on raw observation and reward data. |
Yunfan Zhao; Nikhil Behari; Edward Hughes; Edwin Zhang; Dheeraj Nagaraj; Karl Tuyls; Aparna Taneja; Milind Tambe; |
| 333 | Cross-Domain Feature Augmentation for Domain Generalization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a cross-domain feature augmentation method named XDomainMix that enables us to increase sample diversity while emphasizing the learning of invariant representations to achieve domain generalization. |
Yingnan Liu; Yingtian Zou; Rui Qiao; Fusheng Liu; Mong Li Lee; Wynne Hsu; |
| 334 | On The Pursuit of EFX for Chores: Non-existence and Approximations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of fairly allocating a set of chores to a group of agents. |
Vasilis Christoforidis; Christodoulos Santorinaios; |
| 335 | Learning Causally Disentangled Representations Via The Principle of Independent Causal Mechanisms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. |
Aneesh Komanduri; Yongkai Wu; Feng Chen; Xintao Wu; |
| 336 | VMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building on this analysis, we propose a method called von Mises-Fisher Experience Resampling (vMFER), which optimizes the policy improvement process by resampling transitions and assigning higher confidence to transitions with lower uncertainty of gradient directions. |
Yiwen Zhu; Jinyi Liu; Wenya Wei; Qianyi Fu; Yujing Hu; Zhou Fang; Bo An; Jianye Hao; Tangjie Lv; Changjie Fan; |
| 337 | Deep Learning with Requirements in The Real World Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, I will discuss my proposed methods in the context of two real-world applications: tabular data generation and autonomous driving. |
Mihaela C. Stoian; |
| 338 | A Survey on Rank Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Particularly, to investigate the performance difference of different types of RA methods, we conduct the largest scale of comparative evaluation to date of 27 RA methods on 7 public datasets from person re-identification, recommendation systems, bioinformatics and social choices. |
Siyi Wang; Qi Deng; Shiwei Feng; Hong Zhang; Chao Liang; |
| 339 | Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. |
Kexin Zhang; Feng Huang; Luotao Liu; Zhankun Xiong; Hongyu Zhang; Yuan Quan; Wen Zhang; |
| 340 | Guide to Numerical Experiments on Elections in Computational Social Choice Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We analyze the sizes of the studied elections and the methods of generating preference data, thereby making previously hidden standards and practices explicit. |
Niclas Boehmer; Piotr Faliszewski; Łukasz Janeczko; Andrzej Kaczmarczyk; Grzegorz Lisowski; Grzegorz Pierczyński; Simon Rey; Dariusz Stolicki; Stanisław Szufa; Tomasz Wąs; |
| 341 | DCDet: Dynamic Cross-based 3D Object Detector Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, significant progress has been made in the research of 3D object detection. |
Shuai Liu; Boyang Li; Zhiyu Fang; Kai Huang; |
| 342 | MMGNN: A Molecular Merged Graph Neural Network for Explainable Solvation Free Energy Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the challenge of accurately modeling and predicting Gibbs free energy in solute-solvent interactions, a pivotal yet complex aspect in the field of chemical modeling. |
Wenjie Du; Shuai Zhang; Di Wu; Jun Xia; Ziyuan Zhao; Junfeng Fang; Yang Wang; |
| 343 | Temporal Domain Generalization Via Learning Instance-level Evolving Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, a major obstacle is that datasets at different timestamps may comprise unrelated instances and there is no inherent existence of the instance-level evolving trajectories, which hinders us from learning how the decision boundary changes. To address the above challenges, we propose a Continuous-Time modelling Optimal Transport trajectories (CTOT) framework in this paper. |
Yujie Jin; Zhibang Yang; Xu Chu; Liantao Ma; |
| 344 | CausalNET: Unveiling Causal Structures on Event Sequences By Topology-Informed Causal Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, prevailing methodologies often grapple with untenable assumptions and intricate optimization hurdles. To address these challenges, we present a novel model named CausalNET. |
Hua Zhu; Hong Huang; Kehan Yin; Zejun Fan; Hai Jin; Bang Liu; |
| 345 | Redefining Contributions: Shapley-Driven Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel contribution assessment method called ShapFed for fine-grained evaluation of participant contributions in FL. |
Nurbek Tastan; Samar Fares; Toluwani Aremu; Samuel Horváth; Karthik Nandakumar; |
| 346 | SemanticMask: A Contrastive View Design for Anomaly Detection in Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional techniques, such as random mask, disregard the inter-feature correlations and fail to accurately represent the data. To address this issue, we propose a novel augmentation technique called SemanticMask which leverages the semantic information from column names to generate better augmented views. |
Shuting Tao; Tongtian Zhu; Hongwei Wang; Xiangming Meng; |
| 347 | On Mitigating The Utility-Loss in Differentially Private Learning: A New Perspective By A Geometrically Inspired Kernel Approach (Abstract Reprint) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a geometrically inspired kernel-based approach to mitigate the accuracy-loss issue in classification. |
Mohit Kumar; Bernhard A. Moser; Lukas Fischer; |
| 348 | Joint Source Localization in Different Platforms Via Implicit Propagation Characteristics of Similar Topics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we first construct a multiple platform propagation cascade dataset, aligning similar topics from both Twitter and Weibo, and enriching it with user profiles. Leveraging this dataset, we propose a Dual-channel Source Localization Framework (DSLF) for the joint cascades with similar topics. |
Zhen Wang; Dongpeng Hou; Shu Yin; Chao Gao; Xianghua Li; |
| 349 | Enhancing Length Generalization for Attention Based Knowledge Tracing Models with Linear Biases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose stableKT to enhance length generalization that is able to learn from short sequences and maintain high prediction performance when generalizing on long sequences. |
Xueyi Li; Youheng Bai; Teng Guo; Zitao Liu; Yaying Huang; Xiangyu Zhao; Feng Xia; Weiqi Luo; Jian Weng; |
| 350 | Fair Distribution of Delivery Orders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to fairly distribute delivery costs (modeled as a submodular function) among a fixed set of agents while satisfying some desirable notions of economic efficiency. |
Hadi Hosseini; Shivika Narang; Tomasz Wąs; |
| 351 | DBPNet: Dual-Branch Parallel Network with Temporal-Frequency Fusion for Auditory Attention Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most AAD methods only focus on the temporal or frequency domain, but neglect the relationships between these two domains, which results in the inability to simultaneously consider both time-varying and spectral-spatial information. To address this issue, this paper proposes a dual-branch parallel network with temporal-frequency fusion for AAD, named DBPNet, which consists of the temporal attentive branch and the frequency residual branch. |
Qinke Ni; Hongyu Zhang; Cunhang Fan; Shengbing Pei; Chang Zhou; Zhao Lv; |
| 352 | A Successful Strategy for Multichannel Iterated Prisoner’s Dilemma Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Iterated prisoner’s dilemma (IPD) and its variants are fundamental models for understanding the evolution of cooperation in human society as well as AI systems. In this paper, we focus on multichannel IPD, and examine how an agent should behave to obtain generally high payoffs under this setting. |
Zhen Wang; Zhaoheng Cao; Juan Shi; Peican Zhu; Shuyue Hu; Chen Chu; |
| 353 | Self-Supervised Learning for Enhancing Spatial Awareness in Free-Hand Sketches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, we developed a self-supervised task to correct stroke placement and investigate the impact of spatial layout on learning sketch representations. For this task, we propose a spatially aware method, named SketchGloc, utilizing multiple graphs for graphic sketch representations. |
Xin Wang; Tengjie Li; Sicong Zang; Shikui Tu; Lei Xu; |
| 354 | Online Learning with Off-Policy Feedback in Adversarial MDPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we face the challenge of online learning in adversarial Markov decision processes with off-policy feedback. |
Francesco Bacchiocchi; Francesco Emanuele Stradi; Matteo Papini; Alberto Maria Metelli; Nicola Gatti; |
| 355 | Dual Expert Distillation Network for Generalized Zero-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing studies resort to refining a uniform mapping function to align and correlate the sample regions and subattributes, ignoring two crucial issues: 1) the inherent asymmetry of attributes; and 2) the unutilized channel information. This paper addresses these issues by introducing a simple yet effective approach, dubbed Dual Expert Distillation Network (DEDN), where two experts are dedicated to coarse- and fine-grained visual-attribute modeling, respectively. |
Zhijie Rao; Jingcai Guo; Xiaocheng Lu; Jingming Liang; Jie Zhang; Haozhao Wang; Kang Wei; Xiaofeng Cao; |
| 356 | Negative Prompt Driven Complementary Parallel Representation for Open-World 3D Object Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Negative Prompt Driven Complementary Parallel Representation (NPCP) framework, which navigates the complexities of open-world retrieval through the lens of Negative Prompts. |
Yang Xu; Yifan Feng; Yue Gao; |
| 357 | Automated CPU Design By Learning from Input-Output Examples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an efficient BSD expansion method based on Boolean Distance, a new metric to quantitatively measure the structural similarity between Boolean functions, gradually increasing the design accuracy up to 100%. |
Shuyao Cheng; Pengwei Jin; Qi Guo; Zidong Du; Rui Zhang; Xing Hu; Yongwei Zhao; Yifan Hao; Xiangtao Guan; Husheng Han; Zhengyue Zhao; Ximing Liu; Xishan Zhang; Yuejie Chu; Weilong Mao; Tianshi Chen; Yunji Chen; |
| 358 | Public Event Scheduling with Busy Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, for the general timeline case, we present an algorithmic framework that extends a 1/alpha-approximate algorithm for the one-event instance to the general case that achieves 1/(alpha+1)-approximation. |
Bo Li; Lijun Li; Minming Li; Ruilong Zhang; |
| 359 | Purpose Enhanced Reasoning Through Iterative Prompting: Uncover Latent Robustness of ChatGPT on Code Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is because ChatGPT prioritizes the semantics of code tokens, which makes it vulnerable to commonly encountered benign perturbations such as variable name replacements. This study proposes a modular prompting paradigm Perthept to effectively mitigate the negative effects caused by such minor perturbations. |
Yi Wang; Qidong Zhao; Dongkuan Xu; Xu Liu; |
| 360 | Temporal Knowledge Graph Extrapolation Via Causal Subhistory Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle it, we propose a novel approach called Causal Subhistory Identification (CSI), which focuses on extracting the causal subhistory for reasoning purposes from a large amount of historical data. |
Kai Chen; Ye Wang; Xin Song; Siwei Chen; Han Yu; Aiping Li; |
| 361 | No Regularization Is Needed: Efficient and Effective Incomplete Label Distribution Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the first issue, we adopt a more practical setting, i.e., small degrees are more prone to be missing, since large degrees are likely to catch more attention. |
Xiang Li; Songcan Chen; |
| 362 | Spatial-Temporal Perceiving: Deciphering User Hierarchical Intent in Session-Based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The limited consideration of intent fails to capture complex behavioral patterns in real-world scenarios, leading to sub-optimal solutions. To address this issue, we propose the Hierarchical Intent Perceiving Contrastive Learning Framework (HearInt) for SBR, which proposes a hierarchical consideration of intents from both temporal and spatial perspective. |
Xiao Wang; Tingting Dai; Qiao Liu; Shuang Liang; |
| 363 | ConstrainedZero: Chance-Constrained POMDP Planning Using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work introduces the ConstrainedZero policy iteration algorithm that solves CC-POMDPs in belief space by learning neural network approximations of the optimal value and policy with an additional network head that estimates the failure probability given a belief. |
Robert J. Moss; Arec Jamgochian; Johannes Fischer; Anthony Corso; Mykel J. Kochenderfer; |
| 364 | Sample Quality Heterogeneity-aware Federated Causal Discovery Through Adaptive Variable Space Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Model aggregated under existing FCD methods requires the entire model parameters from each client, thereby being unable to handle the sample quality heterogeneity issue. In this paper, we propose the Federated Adaptive Causal Discovery (FedACD) method to bridge this gap. |
Xianjie Guo; Kui Yu; Hao Wang; Lizhen Cui; Han Yu; Xiaoxiao Li; |
| 365 | TAI++: Text As Image for Multi-Label Image Classification By Co-Learning Transferable Prompt Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, the application scenarios of these methods are limited. In this paper, we propose a pseudo-visual prompt (PVP) module for implicit visual prompt tuning to address this problem. |
Xiangyu Wu; Qing-Yuan Jiang; Yang Yang; Yi-Feng Wu; Qing-Guo Chen; Jianfeng Lu; |
| 366 | A Survey on Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Following that, we provide a systematic overview from both macro and micro views. |
Shu Chen; Zitao Xu; Weike Pan; Qiang Yang; Zhong Ming; |
| 367 | 3D Vision and Language Pretraining with Large-Scale Synthetic Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: 3D Vision-Language Pre-training (3D-VLP) aims to provide a pre-train model which can bridge 3D scenes with natural language, which is an important technique for embodied intelligence. |
Dejie Yang; Zhu Xu; Wentao Mo; Qingchao Chen; Siyuan Huang; Yang Liu; |
| 368 | Learning Label Dependencies for Visual Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous methods often treat the VIE task as a sequence labeling problem and ignore the label correlations in the sequence, which may significantly degrade their performance. To address this issue, this paper proposes a novel framework to exploit the potential of label correlations to improve the VIE models’ performance. |
Minghong Yao; Liansheng Zhuang; Houqiang Li; Jiuchang Wei; |
| 369 | ReliaAvatar: A Robust Real-Time Avatar Animator with Integrated Motion Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The solution we propose to address data-loss scenarios is integrating the full-body avatar pose estimation problem with motion prediction. |
Bo Qian; Zhenhuan Wei; Jiashuo Li; Xing Wei; |
| 370 | Joint Domain Adaptive Graph Convolutional Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These approaches, while effective in mitigating the marginal distribution shift between the two domains, often ignore the integral aspect of structural alignment, potentially leading to negative transfer. To address this issue, we propose a joint adversarial domain adaptive graph convolutional network (JDA-GCN) that is uniquely augmented with structural graph alignment, so as to enhance the efficacy of knowledge transfer. |
Niya Yang; Ye Wang; Zhizhi Yu; Dongxiao He; Xin Huang; Di Jin; |
| 371 | ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-modal Uniform Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing computational methods are not directly applicable to ZS-DDIE, which has two primary challenges: obtaining suitable DDIE representations and handling the class imbalance issue. To overcome these challenges, we propose a novel method named ZeroDDI for the ZS-DDIE task. |
Ziyan Wang; Zhankun Xiong; Feng Huang; Xuan Liu; Wen Zhang; |
| 372 | Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Counterfactual regret minimization (CFR) is a family of algorithms for effectively solving imperfect-information games. It decomposes the total regret into counterfactual regrets, utilizing local regret minimization algorithms, such as Regret Matching (RM) or RM+, to minimize them. |
Hang Xu; Kai Li; Bingyun Liu; Haobo Fu; Qiang Fu; Junliang Xing; Jian Cheng; |
| 373 | CoAtFormer: Vision Transformer with Composite Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce an efficient and effective attention modulewe call Composite Attention. |
Zhiyong Chang; Mingjun Yin; Yan Wang; |
| 374 | Kernel Readout for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper presents a novel graph pooling method called Kernel Readout (KerRead). |
Jiajun Yu; Zhihao Wu; Jinyu Cai; Adele Lu Jia; Jicong Fan; |
| 375 | Rethinking The Soft Conflict Pseudo Boolean Constraint on MaxSAT Local Search Solvers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to transfer the SPB constraint into the clause weighting system of the local search method, leading the algorithm to better solutions. |
Jiongzhi Zheng; Zhuo Chen; Chu-Min Li; Kun He; |
| 376 | Combinatorial Routing for Neural Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these works require every router to provide only one successor for each sample, causing the predictions to be dominated by the elite branch and its derivative architectures. To break this branch dominance, we propose the combinatorial routing neural tree method, termed CombRo. |
Jiahao Li; Ruichu Cai; Yuguang Yan; |
| 377 | SVD-AE: Simple Autoencoders for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, there are no well-designed closed-form studies for balanced CF in terms of the aforementioned trade-offs. In this paper, we design SVD-AE, a simple yet effective singular vector decomposition (SVD)-based linear autoencoder, whose closed-form solution can be defined based on SVD for CF. SVD-AE does not require iterative training processes as its closed-form solution can be calculated at once. |
Seoyoung Hong; Jeongwhan Choi; Yeon-Chang Lee; Srijan Kumar; Noseong Park; |
| 378 | Multi-level Disentangling Network for Cross-Subject Emotion Recognition Based on Multimodal Physiological Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Multi-level Disentangling Network named MDNet for cross-subject emotion recognition based on multimodal physiological signals. |
Ziyu Jia; Fengming Zhao; Yuzhe Guo; Hairong Chen; Tianzi Jiang; |
| 379 | ATTA: Adaptive Test-Time Adaptation for Multi-Modal Sleep Stage Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: 3) How to dynamically adapt the sleep model to different distribution shift in data domains of different subjects. To address these challenges, we propose an Adaptive Test-Time Adaptation (ATTA) method, a multi-modal test-time adaptation method for sleep stage classification. |
Ziyu Jia; Xihao Yang; Chenyang Zhou; Haoyang Deng; Tianzi Jiang; |
| 380 | Revealing Hierarchical Structure of Leaf Venations in Plant Science Via Label-Efficient Segmentation: Dataset and Method Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While current segmentation techniques rely on data-driven models, there is no publicly available dataset specifically designed for hierarchical leaf vein segmentation. To address this gap, we introduce the HierArchical Leaf Vein Segmentation (HALVS) dataset, the first public hierarchical leaf vein segmentation dataset. |
Weizhen Liu; Ao Li; Ze Wu; Yue Li; Baobin Ge; Guangyu Lan; Shilin Chen; Minghe Li; Yunfei Liu; Xiaohui Yuan; Nanqing Dong; |
| 381 | Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection – Towards Precise Fish Morphological Assessment in Aquaculture Breeding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. |
Weizhen Liu; Jiayu Tan; Guangyu Lan; Ao Li; Dongye Li; Le Zhao; Xiaohui Yuan; Nanqing Dong; |
| 382 | From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle the challenge of road network assessment in impoverished areas, we develop a systematic road extraction framework combining an encoder-decoder architecture and morphological operations on satellite imagery, offering an integrated workflow for interdisciplinary researchers. |
Yanxin Xi; Yu Liu; Zhicheng Liu; Sasu Tarkoma; Pan Hui; Yong Li; |
| 383 | Scene-Adaptive Person Search Via Bilateral Modulations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As the scene of moving pedestrian changes, the captured person image inevitably bring in lots of background noise and foreground noise on the person feature, which are completely unrelated to the person identity, leading to severe performance degeneration. To address this issue, we present a Scene-Adaptive Person Search (SEAS) model by introducing bilateral modulations to simultaneously eliminate scene noise and maintain a consistent person representation to adapt to various scenes. |
Yimin Jiang; Huibing Wang; Jinjia Peng; Xianping Fu; Yang Wang; |
| 384 | Predicting Housing Transaction with Common Covariance GNNs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article employs a factor model to analyze data on residents’ rentals, first-time home purchases, and subsequent housing upgrades. |
Jinjin Li; Bin Liu; Chengyan Liu; Hongli Zhang; |
| 385 | Making LLMs As Fine-Grained Relation Extraction Data Augmentor Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the extensive generative capabilities of large language models (LLMs), we introduce a novel framework named ConsistRE, aiming to maintain context consistency in RE. |
Yifan Zheng; Wenjun Ke; Qi Liu; Yuting Yang; Ruizhuo Zhao; Dacheng Feng; Jianwei Zhang; Zhi Fang; |
| 386 | 3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A pressing need exists for a more sophisticated evaluation method capable of thoroughly analyzing the spatial understanding and expressive capabilities of these models. To address these issues, we introduce a scalable 3D benchmark, accompanied by a large-scale instruction-tuning dataset known as 3DBench, providing an extensible platform for a comprehensive evaluation of MLLMs. |
Junjie Zhang; Tianci Hu; Xiaoshui Huang; Yongshun Gong; Dan Zeng; |
| 387 | Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We represent goals as well-formed formulas in first-order logic, effectively bridging intent understanding and optimal behavior planning. |
Xinglin Chen; Yishuai Cai; Yunxin Mao; Minglong Li; Wenjing Yang; Weixia Xu; Ji Wang; |
| 388 | SEMANTIFY: Unveiling Memes with Robust Interpretability Beyond Input Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While Integrated Gradient and similar input-attribution interpretability methods exist, they often yield inadequate and irrelevant keywords. To bridge this gap, we introduce SEMANTIFY, a novel system featuring a theoretically grounded multi-step filtering process. |
Dibyanayan Bandyopadhyay; Asmit Ganguly; Baban Gain; Asif Ekbal; |
| 389 | Unbiased Active Semi-supervised Binary Classification Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop an actively improved augmented estimation equation (AI-AEE) based on corrective weights as well as imputation models that allow us to leverage unlabeled data. |
JooChul Lee; Weidong Ma; Ziyang Wang; |
| 390 | An LLM-enhanced Agent-based Simulation Tool for Information Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an LLM-enhanced Agent-based Influence Diffusion model (LAID), and a web-based visualization tool, LAIDSim, for simulating the information propagation in social networks. |
Yuxuan Hu; Gemju Sherpa; Lan Zhang; Weihua Li; Quan Bai; Yijun Wang; Xiaodan Wang; |
| 391 | A Deep Reinforcement Learning Approach to Balance Viewport Prediction and Video Transmission in 360° Video Streaming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, our measurement shows that three kinds of internal streaming parameters have significant impacts on the prediction distance, namely, download pause, data rate threshold, and playback rate. On top of this, we design a new long-term-planning (LTP) learning method that tunes the parameters dynamically based on the network and streaming context. |
Guanghui Zhang; Jing Guo; |
| 392 | Design A Win-Win Strategy That Is Fair to Both Service Providers and Tasks When Rejection Is Not An Option Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue, we model the problem as an online matching within a bipartite graph and tackle two minimax problems: one focuses on minimizing the highest waiting time of a task, while the other aims to minimize the highest workload of a service provider. |
Yohai Trabelsi; Pan Xu; Sarit Kraus; |
| 393 | Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection Via Proxy-Guided Efficient Re-Sampling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to estimate word generation probabilities as pseudo white-box features via multiple re-sampling to help improve AIGT detection under the black-box setting. |
Yuhui Shi; Qiang Sheng; Juan Cao; Hao Mi; Beizhe Hu; Danding Wang; |
| 394 | Cross-View Contrastive Fusion for Enhanced Molecular Property Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current MPP methods face two prominent challenges: 1) single-view MPP methods do not sufficiently exploit the complementary information of molecular data across multiple views, generally producing suboptimal performance, and 2) most existing multi-view MPP methods ignore the disparities in data quality among different views, inadvertently introducing the risk of models being overshadowed by inferior views. To address the above challenges, we introduce a novel cross-view contrastive fusion for enhanced molecular property prediction method (MolFuse). |
Yan Zheng; Song Wu; Junyu Lin; Yazhou Ren; Jing He; Xiaorong Pu; Lifang He; |
| 395 | STAR: Spatio-Temporal State Compression for Multi-Agent Tasks with Rich Observations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under the assumption of rich observation, we pinpoint that the state representations should be compressed both spatially and temporally to enable efficient prioritization of task-relevant features, while existing works typically fail. To overcome this limitation, we propose a novel method named Spatio-Temporal stAte compRession (STAR) that explicitly defines both spatial and temporal compression operations on the learned state representations to encode per-agent task-relevant features. |
Chao Li; Yujing Hu; Shangdong Yang; Tangjie Lv; Changjie Fan; Wenbin Li; Chongjie Zhang; Yang Gao; |
| 396 | Learning-Based Tracking-before-Detect for RF-Based Unconstrained Indoor Human Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose NeuralTBD, utilizing the capability of deep models and advancement of Tracking-Before-Detect (TBD) methodology to achieve accurate human tracking. |
Zhi Wu; Dongheng Zhang; Zixin Shang; Yuqin Yuan; Hanqin Gong; Binquan Wang; Zhi Lu; Yadong Li; Yang Hu; Qibin Sun; Yan Chen; |
| 397 | Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a deep frequency derivative learning framework, DERITS, for non-stationary time series forecasting. |
Wei Fan; Kun Yi; Hangting Ye; Zhiyuan Ning; Qi Zhang; Ning An; |
| 398 | Continual Multi-View Clustering with Consistent Anchor Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing approaches are designed for fixed multi-view data, and cannot deal with the common streaming data in real world. In this paper, we address this problem by proposing a consistent Anchor guided Continual MVC (ACMVC) method in a two-stage way. |
Chao Zhang; Deng Xu; Xiuyi Jia; Chunlin Chen; Huaxiong Li; |
| 399 | Long Short-Term Dynamic Prototype Alignment Learning for Video Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the lack of mining of temporal dependent relationships and diversified event patterns within videos limit the performance of existing methods. To tackle these problems, we propose a novel prototype-guided and dynamic-aware long-distance frame prediction paradigm for VAD. |
Chao Huang; Jie Wen; Chengliang Liu; Yabo Liu; |
| 400 | ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to provide a personalized gaze estimation model specifically adapted to a target user. |
Yong Wu; Yang Wang; Sanqing Qu; Zhijun Li; Guang Chen; |
| 401 | Towards A Resilient Intelligent Automation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This demo paper presents IDA – our novel UI automation solution, developed to tackle complex resiliency issues. |
Segev Shlomov; Sami Marreed; Avi Yaeli; |
| 402 | LEEC for Judicial Fairness: A Legal Element Extraction Dataset with Extensive Extra-Legal Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To help identify sentencing biases and facilitate downstream applications, we introduce the Legal Element ExtraCtion (LEEC) dataset comprising 15,919 judicial documents and 155 labels. |
Zongyue Xue; Huanghai Liu; Yiran Hu; Yuliang Qian; Yajing Wang; Kangle Kong; Chenlu Wang; Yun Liu; Weixing Shen; |
| 403 | Mitigating Robust Overfitting Via Self-residual-calibration Regularization (Abstract Reprint) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Overfitting in adversarial training has attracted the interest of researchers in the community of artificial intelligence and machine learning in recent years. To address this issue, in this paper we begin by evaluating the defense performances of several calibration methods on various robust models. |
Hong Liu; Zhun Zhong; Nicu Sebe; Shin’ichi Satoh; |
| 404 | Invertible Residual Rescaling Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we observe that IRNs with deeper networks are difficult to train, thus hindering the representational ability of IRNs. To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution. |
Jinmin Li; Tao Dai; Yaohua Zha; Yilu Luo; Longfei Lu; Bin Chen; Zhi Wang; Shu-Tao Xia; Jingyun Zhang; |
| 405 | GenSeg: On Generating Unified Adversary for Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose GenSeg, a Generative paradigm that creates unified adversaries for Segmentation tasks. |
Yuxuan Zhang; Zhenbo Shi; Wei Yang; Shuchang Wang; Shaowei Wang; Yinxing Xue; |
| 406 | LocMoE: A Low-overhead MoE for Large Language Model Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The frequent inter-node communication in the All-to-All procedure also significantly prolongs the training time. To alleviate the above performance problems, we propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. |
Jing Li; Zhijie Sun; Xuan He; Li Zeng; Yi Lin; Entong Li; Binfan Zheng; Rongqian Zhao; Xin Chen; |
| 407 | General Epistemic Abstract Argumentation Framework: Semantics and Complexity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Epistemic Abstract Argumentation Framework (EAAF) extends Dung’s framework (AAF)—a central formalism in AI for modeling disputes among agents—by allowing the representation of epistemic knowledge.In particular, EAAF augments AAF with weak and strong epistemic attacks whose intuitive meaning is that an argument a defeats an argument b by means of a weak (resp. |
Gianvincenzo Alfano; Sergio Greco; Francesco Parisi; Irina Trubitsyna; |
| 408 | Visual Attention Prompted Prediction and Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a novel framework for visual attention prompted prediction and learning, utilizing visual prompts to steer the model’s reasoning process. To improve performance in non-prompted situations and align it with prompted scenarios, we propose a co-training approach for both non-prompted and prompted models, ensuring they share similar parameters and activation. |
Yifei Zhang; Bo Pan; Siyi Gu; Guangji Bai; Meikang Qiu; Xiaofeng Yang; Liang Zhao; |
| 409 | GladCoder: Stylized QR Code Generation with Grayscale-Aware Denoising Process Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel approach GladCoder to generate stylized QR codes that are personalized, natural, and text-driven. |
Yuqiu Xie; Bolin Jiang; Jiawei Li; Naiqi Li; Bin Chen; Tao Dai; Yuang Peng; Shu-Tao Xia; |
| 410 | Self-adaptive PSRO: Towards An Automatic Population-based Game Solver Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (3) To overcome the poor performance of online HPO methods, we propose a novel offline HPO approach to optimize the HPO policy based on the Transformer architecture. |
Pengdeng Li; Shuxin Li; Chang Yang; Xinrun Wang; Xiao Huang; Hau Chan; Bo An; |
| 411 | What Hides Behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear. In this paper, we address this gap in the literature by investigating the sources of inequality through a causal lens. |
Zhihong Deng; Jing Jiang; Guodong Long; Chengqi Zhang; |
| 412 | EVE: Efficient Zero-Shot Text-Based Video Editing With Depth Map Guidance and Temporal Consistency Constraints Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Compared with images, we conjecture that videos necessitate more constraints to preserve the temporal consistency during editing. Towards this end, we propose EVE, a robust and Efficient zero-shot Video Editing method. |
Yutao Chen; Xingning Dong; Tian Gan; Chunluan Zhou; Ming Yang; Qingpei Guo; |
| 413 | On The Computation of Example-Based Abductive Explanations for Random Forests Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We prove that the latter coverage condition yields a complexity shift to the second level of the polynomial hierarchy. We present a CEGAR-based algorithm to derive such explanations, and show how to modify it to derive most anchored example-based abductive explanations, i.e., example-based abductive explanations that cover as many reference instances as possible. |
Gilles Audemard; Jean-Marie Lagniez; Pierre Marquis; Nicolas Szczepanski; |
| 414 | Deriving Provably Correct Explanations for Decision Trees: The Impact of Domain Theories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify the complexity of deriving local explanations (abductive or contrastive) given a decision tree in the general case, and under several natural restrictions about the domain theory. |
Gilles Audemard; Jean-Marie Lagniez; Pierre Marquis; Nicolas Szczepanski; |
| 415 | GRASP: A Novel Benchmark for Evaluating Language GRounding and Situated Physics Understanding in Multimodal Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents GRASP, a novel benchmark to evaluate the language grounding and physical understanding capabilities of video-based multimodal large language models (LLMs). |
Serwan Jassim; Mario Holubar; Annika Richter; Cornelius Wolff; Xenia Ohmer; Elia Bruni; |
| 416 | PyXAI: An XAI Library for Tree-Based Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is suited to decision trees, random forests, and boosted trees, when used for regression or classification tasks. In contrast to many model-agnostic approaches to XAI, PyXAI exploits the model it- self to generate explanations, ensuring them to be faithful. |
Gilles Audemard; Jean-Marie Lagniez; Pierre Marquis; Nicolas Szczepanski; |
| 417 | Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the limitation, we propose AnomalyLLM, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets. |
Chen Liu; Shibo He; Qihang Zhou; Shizhong Li; Wenchao Meng; |
| 418 | Place Anything Into Any Video Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel and efficient system named Place-Anything, which facilitates the insertion of any object into any video solely based on a picture or text description of the target object or element. |
Ziling Liu; Jinyu Yang; Mingqi Gao; Feng Zheng; |
| 419 | KDDC: Knowledge-Driven Disentangled Causal Metric Learning for Pre-Travel Out-of-Town Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are still two challenges that hamper the performance of these approaches: i) Users’ interactive data (including hometown and out-of-town check-ins) tend to be rare, and while candidate POIs that come from different regions contain various semantic information; ii) The causes for user check-in include not only interest but also conformity, which are easily entangled and overlooked. To fill these gaps, we propose a Knowledge-Driven Disentangled Causal metric learning framework (KDDC) that mitigates interaction data sparsity by enhancing POI semantic representation and considers the distributions of two causes (i.e., conformity and interest) for pre-travel recommendation. |
Yinghui Liu; Guojiang Shen; Chengyong Cui; Zhenzhen Zhao; Xiao Han; Jiaxin Du; Xiangyu Zhao; Xiangjie Kong; |
| 420 | SceneDiff: Generative Scene-Level Image Retrieval with Text and Sketch Using Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose SceneDiff, a novel retrieval network that leverages a pre-trained diffusion model to establish a shared generative latent space, enabling a joint latent representation learning for both sketch and text features and precise alignment with the corresponding image. |
Ran Zuo; Haoxiang Hu; Xiaoming Deng; Cangjun Gao; Zhengming Zhang; Yu-Kun Lai; Cuixia Ma; Yong-Jin Liu; Hongan Wang; |
| 421 | AI-Olympics: Exploring The Generalization of Agents Through Open Competitions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We aim to contribute insights to the field of multi-agent decision-making and explore the generalization of agents through engineering efforts. |
Chen Wang; Yan Song; Shuai Wu; Sa Wu; Ruizhi Zhang; Shu Lin; Haifeng Zhang; |
| 422 | Unified Physical-Digital Face Attack Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main reasons for this lack of an integrated model are caused by two factors: (1) The lack of a dataset including both physical and digital attacks which the same ID covers the real face and all attack types; (2) Given the large intra-class variance between these two attacks, it is difficult to learn a compact feature space to detect both attacks simultaneously. To address these issues, we collect a Unified physical-digital Attack dataset, called UniAttackData. |
Hao Fang; Ajian Liu; Haocheng Yuan; Junze Zheng; Dingheng Zeng; Yanhong Liu; Jiankang Deng; Sergio Escalera; Xiaoming Liu; Jun Wan; Zhen Lei; |
| 423 | LG-FGAD: An Effective Federated Graph Anomaly Detection Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing GAD methods are generally designed for centralized training, while in real-world collaboration, graph data is generally distributed across various clients and exhibits significant non-IID characteristics. To tackle this challenge, we propose a federated graph anomaly detection framework with local-global anomaly awareness (LG-FGAD). |
Jinyu Cai; Yunhe Zhang; Jicong Fan; See-Kiong Ng; |
| 424 | Heterogeneous Graph Transformer with Poly-Tokenization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, existing graph transformers struggle to capture the long-range dependencies in these complex heterogeneous graphs. To address these two limitations, we present Poly-tokenized Heterogeneous Graph Transformer (PHGT), a novel transformer-based heterogeneous graph model. |
Zhiyuan Lu; Yuan Fang; Cheng Yang; Chuan Shi; |
| 425 | Multi-TA: Multilevel Temporal Augmentation for Robust Septic Shock Early Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces Multi-TA, a multilevel temporal augmentation framework that leverages BERT-based temporal EHRs representation learning and a unified data augmentation approach, effectively addressing data scarcity issues at both event and trajectory levels. |
Hyunwoo Sohn; Kyungjin Park; Baekkwan Park; Min Chi; |
| 426 | Unlearning from Weakly Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the uniform distributions of untrained model predictions, we derive a formulated target to force the model’s predictions of removed data to be indistinguishable. |
Yi Tang; Yi Gao; Yong-gang Luo; Ju-Cheng Yang; Miao Xu; Min-Ling Zhang; |
| 427 | Contrastive Transformer Masked Image Hashing for Degraded Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the prevalence of degraded images on social media platforms, resulting from imperfections in the image capture process, poses new challenges for conventional image retrieval methods. To address this issue, we propose Contrastive Transformer Masked Image Hashing (CTMIH), a novel deep unsupervised hashing method specifically designed for degraded image retrieval, which is challenging yet relatively less studied. |
Xiaobo Shen; Haoyu Cai; Xiuwen Gong; Yuhui Zheng; |
| 428 | Contrastive Transformer Cross-Modal Hashing for Video-Text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, when applied to video-text retrieval, existing cross-modal hashing methods generally extract features at the frame- or word-level for videos and texts individually, thereby ignoring their long-term dependencies. To address this issue, we propose Contrastive Transformer Cross-Modal Hashing (CTCH), a novel approach designed for video-text retrieval task. |
Xiaobo Shen; Qianxin Huang; Long Lan; Yuhui Zheng; |
| 429 | Automated Essay Scoring: Recent Successes and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey paper discusses the milestones in AES research and reflects on future directions. |
Shengjie Li; Vincent Ng; |
| 430 | Global Optimality of Single-Timescale Actor-Critic Under Continuous State-Action Space: A Study on Linear Quadratic Regulator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We push the boundary to investigate the classic single-sample single-timescale actor-critic on continuous (infinite) state-action space, where we employ the canonical linear quadratic regulator (LQR) problem as a case study. |
Xuyang Chen; Jingliang Duan; Lin Zhao; |
| 431 | Probabilistic Contrastive Learning for Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We find that this is mainly because the class weights (weights of the final fully connected layer) are ignored in the domain adaptation optimization process, which makes it difficult for features to cluster around the corresponding class weights. To solve this problem, we propose the simple but powerful Probabilistic Contrastive Learning (PCL), which moves beyond the standard paradigm by removing l2 normalization and replacing the features with probabilities. |
Junjie Li; Yixin Zhang; Zilei Wang; Saihui Hou; Keyu Tu; Man Zhang; |
| 432 | Enhancing Cross-Modal Retrieval Via Visual-Textual Prompt Hashing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the majority of existing methods suffer from the limitations of context loss and information redundancy, particularly in simulated textual environments enriched with manually annotated tags or virtual descriptions. To mitigate these issues, we propose a novel Visual-Textual Prompt Hashing (VTPH) that aims to bridge the gap between simulated textual and visual modalities within a unified prompt optimization paradigm for cross-modal retrieval. |
Bingzhi Chen; Zhongqi Wu; Yishu Liu; Biqing Zeng; Guangming Lu; Zheng Zhang; |
| 433 | Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formalize the active voltage control problem as a constrained Markov game and propose a safety-constrained MARL algorithm. |
Yang Qu; Jinming Ma; Feng Wu; |
| 434 | Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Feature Norm Regularized Federated Learning (FNR-FL) algorithm to tackle the non-i.i.d challenge. |
Ke Hu; Liyao Xiang; Peng Tang; Weidong Qiu; |
| 435 | Meta-Learning Via PAC-Bayesian with Data-Dependent Prior: Generalization Bounds from Local Entropy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous works still encounter two notable limitations: (1) they merely focus on the data-free priors, which often result in inappropriate regularization and loose generalization bounds; (2) more importantly, their optimization process usually involves nested optimization problems, incurring significant computational costs. To address these issues, we derive new generalization bounds and introduce a novel PAC-Bayesian framework for meta-learning that integrates data-dependent priors. |
Shiyu Liu; Wei Shi; Zenglin Xu; Shaogao Lv; Yehong Zhang; Hui Wang; |
| 436 | Rank and Align: Towards Effective Source-free Graph Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inharmonic graphs with harmonic graphs which close to the source domain for subgraph extraction. |
Junyu Luo; Zhiping Xiao; Yifan Wang; Xiao Luo; Jingyang Yuan; Wei Ju; Langechuan Liu; Ming Zhang; |
| 437 | Unified Single-Stage Transformer Network for Efficient RGB-T Tracking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, the three-stage fusion tracking paradigm followed by these networks significantly restricts the tracking speed. To overcome these problems, we propose a unified single-stage Transformer RGB-T tracking network, namely USTrack, which unifies the above three stages into a single ViT (Vision Transformer) backbone through joint feature extraction, fusion and relation modeling. |
Jianqiang Xia; Dianxi Shi; Ke Song; Linna Song; Xiaolei Wang; Songchang Jin; Chenran Zhao; Yu Cheng; Lei Jin; Zheng Zhu; Jianan Li; Gang Wang; Junliang Xing; Jian Zhao; |
| 438 | A Strategic Analysis of Prepayments in Financial Credit Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, our focus shifts to understanding the strategic behavior of individual firms in the presence of prepayments. We introduce a prepayment game where firms strategically make prepayments, delineating the existence of pure strategy Nash equilibria and analyzing the price of anarchy (stability) within this game. |
Hao Zhou; Yongzhao Wang; Konstantinos Varsos; Nicholas Bishop; Rahul Savani; Anisoara Calinescu; Michael Wooldridge; |
| 439 | MCM: Multi-condition Motion Synthesis Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose a multi-condition HMS framework, termed MCM, based on a dual-branch structure composed of a main branch and a control branch. |
Zeyu Ling; Bo Han; Yongkang Wong; Han Lin; Mohan Kankanhalli; Weidong Geng; |
| 440 | Selective Learning for Sample-Efficient Training in Multi-Agent Sparse Reward Tasks (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper focuses on sparse-reward multi-agent cooperative tasks and proposes an effective experience-sharing method, Multi-Agent Selective Learning (MASL), to boost sample-efficient training by reusing valuable experiences from other agents. |
Xinning Chen; Xuan Liu; Yanwen Ba; Shigeng Zhang; Bo Ding; Kenli Li; |
| 441 | Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper discusses various aspects of modern recommender systems, focusing on challenges such as preference elicitation, the complexity of human decision-making, and multi-domain applicability. |
Julia Neidhardt; |
| 442 | Hacking Task Confounder in Meta-Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We refer to these confounding factors as “Task Confounders". Based on these findings, we propose a plug-and-play Meta-learning Causal Representation Learner (MetaCRL) to eliminate task confounders. |
Jingyao Wang; Yi Ren; Zeen Song; Jianqi Zhang; Changwen Zheng; Wenwen Qiang; |
| 443 | DFRP: A Dual-Track Feedback Recommendation System for Educational Resources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most of the existing recommendation algorithms only consider interaction history, while we argue that the dependencies between knowledge points and education-related features are crucial for education resource recommendations. To address this, we propose DFRP, an educational resource recommendation platform based on knowledge graphs(KGs) and educational scale feedback. |
ChaoJun Meng; Changfan Pan; Zilong Li; Cong Zhou; Xinran Cao; Jia Zhu; |
| 444 | Getting More By Knowing Less: Bayesian Incentive Compatible Mechanisms for Fair Division Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study fair resource allocation with strategic agents. |
Vasilis Gkatzelis; Alexandros Psomas; Xizhi Tan; Paritosh Verma; |
| 445 | Exploring Urban Semantics: A Multimodal Model for POI Semantic Annotation with Street View Images and Place Names Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to explore semantic annotation for POIs with limited information such as POI (place) names and geographic locations. |
Dabin Zhang; Meng Chen; Weiming Huang; Yongshun Gong; Kai Zhao; |
| 446 | Pointsoup: High-Performance and Extremely Low-Decoding-Latency Learned Geometry Codec for Large-Scale Point Cloud Scenes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Pointsoup, an efficient learning-based geometry codec that attains high-performance and extremely low-decoding-latency simultaneously. |
Kang You; Kai Liu; Li Yu; Pan Gao; Dandan Ding; |
| 447 | Oasis: Data Curation and Assessment System for Pretraining of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present a pretraining corpus curation and assessment platform called Oasis — a one-stop system for data quality improvement and quantification with user-friendly interactive interfaces. |
Tong Zhou; Yubo Chen; Pengfei Cao; Kang Liu; Shengping Liu; Jun Zhao; |
| 448 | Temporal Inductive Logic Reasoning Over Hypergraphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose temporal inductive logic reasoning (TILR), an ILP method that reasons on temporal hypergraphs. |
Yuan Yang; Siheng Xiong; Ali Payani; James C. Kerce; Faramarz Fekri; |
| 449 | NegativePrompt: Leveraging Psychology for Large Language Models Enhancement Via Negative Emotional Stimuli Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This discovery raises an intriguing question: can negative emotions similarly influence LLMs, potentially enhancing their performance? In response to this question, we introduce NegativePrompt, a novel approach underpinned by psychological principles, involving ten specifically designed negative emotional stimuli. |
Xu Wang; Cheng Li; Yi Chang; Jindong Wang; Yuan Wu; |
| 450 | Innovative Directional Encoding in Speech Processing: Leveraging Spherical Harmonics Injection for Multi-Channel Speech Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose using spherical harmonics transform (SHT) coefficients as auxiliary inputs to models. |
Jiahui Pan; Pengjie Shen; Hui Zhang; Xueliang Zhang; |
| 451 | Detector Collapse: Backdooring Object Detection to Catastrophic Overload or Blindness in The Physical World Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. |
Hangtao Zhang; Shengshan Hu; Yichen Wang; Leo Yu Zhang; Ziqi Zhou; Xianlong Wang; Yanjun Zhang; Chao Chen; |
| 452 | KALE: An Artwork Image Captioning System Augmented with Heterogeneous Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The task is particularly complex for artwork images due to their diverse interpretations and varied aesthetic principles across different artistic schools and styles. In response to this, we present KALE (Knowledge-Augmented vision-Language model for artwork Elaborations), a novel approach that enhances existing vision-language models by integrating artwork metadata as additional knowledge. |
Yanbei Jiang; Krista A. Ehinger; Jey Han Lau; |
| 453 | POWL: Partially Ordered Workflow Language (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While partial orders capture both concurrent and sequential interactions among activities in a compact way, they fall short in modeling choice and cyclic behavior. To address this gap, we introduce the Partially Ordered Workflow Language (POWL), a novel language for process modeling that combines traditional hierarchical modeling languages with partial orders. |
Humam Kourani; Sebastiaan van Zelst; |
| 454 | Exploiting Cultural Biases Via Homoglyphs InText-to-Image Synthesis (Abstract Reprint) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One goal might be to create racist stereotypes by replacing Latin characters with similarly-looking characters from non-Latin scripts, so-called homoglyphs. To mitigate such unnoticed script attacks, we propose a novel homoglyph unlearning method to fine-tune a text encoder, making it robust against homoglyph manipulations. |
Lukas Struppek; Dominik Hintersdorf; Felix Friedrich; Manuel Brack; Patrick Schramowski; Kristian Kersting; |
| 455 | Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries.Concurrently, we propose an innovative approach: Art Assessment Network for Specific Painting Styles (AANSPS), designed for the assessment of aesthetic attributes in mixed-attribute art datasets. |
Xin Jin; Qianqian Qiao; Yi Lu; Huaye Wang; Shan Gao; Heng Huang; Guangdong Li; |
| 456 | Dialogue Cross-Enhanced Central Engagement Attention Model for Real-Time Engagement Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, those studies focus mainly on the target participants’ features without taking into account those of the interlocutor. To address these issues, we propose the center-based sliding window method to obtain feature subsequences. |
Jun Yu; Keda Lu; Ji Zhao; Zhihong Wei; Iek-Heng Chu; Peng Chang; |
| 457 | Dynamic Against Dynamic: An Open-Set Self-Learning Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In fact, such samples completely can constitute the true instantiated representation of the unknown classes to further enhance the model’s performance. To address these issues, this paper proposes a novel dynamic against dynamic idea, i.e., dynamic method against dynamic changing open-set world, where an open-set self-learning (OSSL) framework is correspondingly developed. |
Haifeng Yang; Chuanxing Geng; Pong C. Yuen; Songcan Chen; |
| 458 | Exploring The Inefficiency of Heavy Ball As Momentum Parameter Approaches 1 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the theoretical findings, we propose a descending warmup technique for the heavy ball momentum, which exploits the advantages of the heavy ball method and overcomes the inefficiency problem when the momentum tends to 1. |
Xiaoge Deng; Tao Sun; Dongsheng Li; Xicheng Lu; |
| 459 | Explaining Arguments’ Strength: Unveiling The Role of Attacks and Supports Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel theory of Relation Attribution Explanations (RAEs), adapting Shapley values from game theory to offer fine-grained insights into the role of attacks and supports in quantitative bipolar argumentation towards obtaining the arguments’ strength. |
Xiang Yin; Nico Potyka; Francesca Toni; |
| 460 | ClothPPO: A Proximal Policy Optimization Enhancing Framework for Robotic Cloth Manipulation with Observation-Aligned Action Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce ClothPPO, a framework that employs a policy gradient algorithm based on actor-critic architecture to enhance a pre-trained model with huge 10^6 action spaces aligned with observation in the task of unfolding clothes. |
Libing Yang; Yang Li; Long Chen; |
| 461 | Deep Embedding Clustering Driven By Sample Stability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This construction process requires some prior knowledge, and it is challenging to determine a suitable pseudo target for clustering. To address this issue, we propose a deep embedding clustering algorithm driven by sample stability (DECS), which eliminates the requirement of pseudo targets. |
Zhanwen Cheng; Feijiang Li; Jieting Wang; Yuhua Qian; |
| 462 | A Self-explaining Neural Architecture for Generalizable Concept Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in this paper, we demonstrate that present SOTA concept learning approaches suffer from two major problems – lack of concept fidelity wherein the models fail to learn consistent concepts among similar classes and limited concept interoperability wherein the models fail to generalize learned concepts to new domains for the same task. Keeping these in mind, we propose a novel self-explaining architecture for concept learning across domains which – i) incorporates a new concept saliency network for representative concept selection, ii) utilizes contrastive learning to capture representative domain invariant concepts, and iii) uses a novel prototype-based concept grounding regularization to improve concept alignment across domains. |
Sanchit Sinha; Guangzhi Xiong; Aidong Zhang; |
| 463 | Multi-Granularity Graph-Convolution-Based Method for Weakly Supervised Person Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this task is very challenging due to the following reasons: 1) large feature gap between person ReID and general object detection tasks when learning shared representations; 2) difficult pseudo identity estimation for each person image with unrefined raw detection and dramatic scale changes. To address above issues, we propose a multi-granularity graph convolution framework to jointly optimize the aligned task features, as well as to assist the pseudo label estimation. |
Haichun Tai; De Cheng; Jie Li; Nannan Wang; Xinbo Gao; |
| 464 | ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving The Traveling Thief Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the first reinforcement learning based multi-start neighborhood search algorithm, denoted by ReinforceNS, for solving TTP. |
Tao Wu; Huachao Cui; Tao Guan; Yuesong Wang; Yan Jin; |
| 465 | Multi-Relational Graph Attention Network for Social Relationship Inference from Human Mobility Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their effectiveness, we argue that previous methods either rely solely on direct relations between users, neglecting valuable user mobility patterns, or have not fully harnessed the indirect interactions, thereby struggling to capture users’ mobility preferences. To address these issues, in this work, we propose the Multi-Relational Graph Attention Network (MRGAN), a novel graph attention network, which is able to explicitly model indirect relations and effectively capture their different impact. |
Guangming Qin; Jianpeng Qi; Bin Wang; Guiyuan Jiang; Yanwei Yu; Junyu Dong; |
| 466 | Cross-Domain Few-Shot Semantic Segmentation Via Doubly Matching Transformation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, they completely rely on support images for feature transformation, and repeatedly utilizing a few support images for each class may easily lead to overfitting and overlooking intra-class appearance differences. In this paper, we propose a Doubly Matching Transformation-based Network (DMTNet) to solve the above issue. |
Jiayi Chen; Rong Quan; Jie Qin; |
| 467 | Task-Agnostic Self-Distillation for Few-Shot Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, the task-specific metric feature can decrease the generalization ability and ignore the general matching feature applicable across different tasks. To address these challenges, we propose a novel meta-distillation framework for few-shot action recognition that learns the task-agnostic metric features and generalizes them to different tasks. |
Bin Zhang; Yuanjie Dang; Peng Chen; Ronghua Liang; Nan Gao; Ruohong Huan; Xiaofei He; |
| 468 | Personalized Heart Disease Detection Via ECG Digital Twin Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals’ anomalous ECGs and enhances the model sensitivity to the personalized symptoms. |
Yaojun Hu; Jintai Chen; Lianting Hu; Dantong Li; Jiahuan Yan; Haochao Ying; Huiying Liang; Jian Wu; |
| 469 | Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose the BilEvel Transfer AttacK (BETAK) framework by establishing an initialization derived bilevel optimization paradigm, which explicitly reformulates the nested constraint relationship between the Upper-Level (UL) pseudo-victim attacker and the Lower-Level (LL) surrogate attacker. |
Yaohua Liu; Jiaxin Gao; Xuan Liu; Xianghao Jiao; Xin Fan; Risheng Liu; |
| 470 | Unified Unsupervised Salient Object Detection Via Knowledge Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a unified USOD framework for generic USOD tasks. |
Yao Yuan; Wutao Liu; Pan Gao; Qun Dai; Jie Qin; |
| 471 | Trade When Opportunity Comes: Price Movement Forecasting Via Locality-Aware Attention and Iterative Refinement Labeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing ML methods, however, struggle with the extremely low signal-to-noise ratio and stochastic nature of financial data, often mistaking noises for real trading signals without careful selection of potentially profitable samples. To address this issue, we propose LARA, a novel price movement forecasting framework with two main components: Locality-Aware Attention (LA-Attention) and Iterative Refinement Labeling (RA-Labeling). |
Liang Zeng; Lei Wang; Hui Niu; Ruchen Zhang; Ling Wang; Jian Li; |
| 472 | Carbon Market Simulation with Adaptive Mechanism Design Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the complexity of carbon market dynamics makes accurate simulation intractable, which in turn hinders the design of effective allocation strategies. To address this, we propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL). |
Han Wang; Wenhao Li; Hongyuan Zha; Baoxiang Wang; |
| 473 | GUIDE: A Guideline-Guided Dataset for Instructional Video Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the specific steps without guidelines are trivial and unsystematic, making it difficult to provide a clear tutorial. To address these problems, we present the Guide (Guideline-Guided) dataset, which contains 3.5K videos of 560 instructional tasks in 8 domains related to our daily life. |
Jiafeng Liang; Shixin Jiang; Zekun Wang; Haojie Pan; Zerui Chen; Zheng Chu; Ming Liu; Ruiji Fu; Zhongyuan Wang; Bing Qin; |
| 474 | NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, we propose a novel batch size-agnostic method called NanoAdapt to effectively mitigate the negative transfer even with batch size 1. |
Shiji Zhao; Shao-Yuan Li; Sheng-Jun Huang; |
| 475 | Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a novel benchmark for assessing QSR in LMs, which is grounded in realistic 3D simulation data, offering a series of diverse room layouts with various objects and their spatial relationships. |
Fangjun Li; David C. Hogg; Anthony G. Cohn; |
| 476 | Empathy and AI: Achieving Equitable Microtransit for Underserved Communities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes a newly launched project that will produce a new approach to public microtransit for underserved communities. |
Eleni Bardaka; Pascal Van Hentenryck; Crystal Chen Lee; Christopher B. Mayhorn; Kai Monast; Samitha Samaranayake; Munindar P. Singh; |
| 477 | Robust Contrastive Multi-view Kernel Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, its inherent drawback is the potential inappropriate amplification of distances between different instances of the same clusters (i.e., false negative pairs) during the training process, leading to a reduction in inter-class discriminability. To address this challenge, we propose a Robust Contrastive multi-view kernel Learning approach (R-CMK) against false negative pairs. |
Peng Su; Yixi Liu; Shujian Li; Shudong Huang; Jiancheng Lv; |
| 478 | Efficient and Stable Offline-to-online Reinforcement Learning Via Continual Policy Revitalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Continual Policy Revitalization (CPR) as a novel efficient, stable fine-tuning method. |
Rui Kong; Chenyang Wu; Chen-Xiao Gao; Zongzhang Zhang; Ming Li; |
| 479 | Exploring Cross-Domain Few-Shot Classification Via Frequency-Aware Prompting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence in this paper, we make one of the first attempts to propose a Frequency-Aware Prompting method with mutual attention for Cross-Domain Few-Shot classification, which can let networks simulate the human visual perception of selecting different frequency cues when facing new recognition tasks. |
Tiange Zhang; Qing Cai; Feng Gao; Lin Qi; Junyu Dong; |
| 480 | Proof Logging for Smart Extensional Constraints (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we demonstrate how to support proof logging for a wide range of previously uncertified global constraints. |
Matthew J. McIlree; Ciaran McCreesh; |
| 481 | NeuroSymbolic LLM for Mathematical Reasoning and Software Engineering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our objective, however, is to pursue a different path by developing neurosymbolic language models. |
Prithwish Jana; |
| 482 | Formal Argumentation in Symbolic AI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper I describe my most significant contributions to the field spanning from general non-monotonic logics to formal argumentation. |
Markus Ulbricht; |
| 483 | OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores. |
Yang Li; Qiuyi Huang; Chong Zhong; Danjuan Yang; Meiyan Li; A.H. Welsh; Aiyi Liu; Bo Fu; Catherine C. Liu; Xingtao Zhou; |
| 484 | A Teacher Classroom Dress Assessment Method Based on A New Assessment Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to verify the effectiveness of the dataset, this paper systematically explores a new perspective on human attribute information and proposes for the first time a Teachers’ Dress Assessment Method (TDAM), aiming to use predicted teacher attributes to scoring the overall attire of each teacher, thereby promoting the development of the teacher’s classroom teaching field. |
Ming Fang; Qi Liu; Yunpeng Zhou; Xinning Du; Qiwen Liang; Shuhua Liu; |
| 485 | An Efficient Prototype-Based Clustering Approach for Edge Pruning in Graph Neural Networks to Battle Over-Smoothing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While clustering offers an alternative for identifying closely connected groups of nodes, traditional clustering methods face challenges when applied to GNNs in terms of accuracy, efficiency, adaptability, and scalability to diverse graphs. To address these limitations, we introduce ClusterDrop, which uses learnable prototypes for efficient clustering and incorporates supervised signals to enhance accuracy and adaptability across different graphs. |
Yuyang Huang; Wenjing Lu; Yang Yang; |
| 486 | End-to-End Real-World Polyphonic Piano Audio-to-Score Transcription with Hierarchical Decoding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To preserve the voicing structure for score reconstruction, we propose a pre-processing method for **Kern scores in scenarios with an unconstrained number of voices. |
Wei Zeng; Xian He; Ye Wang; |
| 487 | Continual Multi-Objective Reinforcement Learning Via Reward Model Rehearsal Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, we propose Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal (CORe3), incorporating a dynamic agent network for rapid adaptation to new objectives. |
Lihe Li; Ruotong Chen; Ziqian Zhang; Zhichao Wu; Yi-Chen Li; Cong Guan; Yang Yu; Lei Yuan; |
| 488 | Hyperparameter Optimization Can Even Be Harmful in Off-Policy Learning and How to Deal with It Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work explores the latter hyperparameter optimization (HPO) task for off-policy learning. |
Yuta Saito; Masahiro Nomura; |
| 489 | A Little of That Human Touch: Achieving Human-Centric Explainable AI Via Argumentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: If we are to comprehend and trust these AI models as they advance, it is clear that symbolic methods, given their unparalleled strengths in knowledge representation and reasoning, can play an important role in explaining AI models. In this paper, I discuss some of the ways in which one branch of such methods, computational argumentation, given its human-like nature, can be used to tackle this problem. |
Antonio Rago; |
| 490 | Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To consider the dependencies among class variables and the specific characteristics contained in different semantic dimensions, a novel deep MDC approach named PIST is proposed to jointly deal with the two issues via learning pairwise dimension-specific features. |
Teng Huang; Bin-Bin Jia; Min-Ling Zhang; |
| 491 | Efficient Offline Meta-Reinforcement Learning Via Robust Task Representations and Adaptive Policy Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an OMRL algorithm to tackle the aforementioned issues. |
Zhengwei Li; Zhenyang Lin; Yurou Chen; Zhiyong Liu; |
| 492 | Skip-Timeformer: Skip-Time Interaction Transformer for Long Sequence Time-Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose Skip-Timeformer, a Transformer-based model that utilizes a skip-time interaction for long sequence time-series forecasting. |
Wenchang Zhang; Hua Wang; Fan Zhang; |
| 493 | Common-Individual Semantic Fusion for Multi-View Multi-Label Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take a new perspective and propose a new semantic-level fusion model named Common-Individual Semantic Fusion Multi-View Multi-Label Learning Method (CISF). |
Gengyu Lyu; Weiqi Kang; Haobo Wang; Zheng Li; Zhen Yang; Songhe Feng; |
| 494 | Towards Sharper Generalization Bounds for Adversarial Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the empirical success, its generalization behavior remains poorly understood and far from being well-characterized. This paper aims to address this issue from a learning theory perspective. |
Wen Wen; Han Li; Tieliang Gong; Hong Chen; |
| 495 | Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes Imperio, which harnesses the language understanding capabilities of NLP models to enrich backdoor attacks. |
Ka-Ho Chow; Wenqi Wei; Lei Yu; |
| 496 | Denoising Diffusion-Augmented Hybrid Video Anomaly Detection Via Reconstructing Noised Frames Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although existing methods have achieved decent detection performances on benchmarks, their predicted objects still remain ambiguous in terms of the semantic aspect. To overcome this limitation, we propose the Denoising diffusion-augmented Hybrid Video Anomaly Detection (DHVAD) framework. |
Kai Cheng; Yaning Pan; Yang Liu; Xinhua Zeng; Rui Feng; |
| 497 | Langshaw: Declarative Interaction Protocols Based on Sayso and Conflict Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Langshaw, a declarative protocol language based on (1) sayso, a new construct that captures who has priority over setting each attribute, and (2) nono and nogo, two constructs to capture conflicts between actions. |
Munindar P. Singh; Samuel H. Christie V.; Amit K. Chopra; |
| 498 | Exterior Penalty Policy Optimization with Penalty Metric Network Under Constraints Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a theoretically guaranteed penalty function method, Exterior Penalty Policy Optimization (EPO), with adaptive penalties generated by a Penalty Metric Network (PMN). |
Shiqing Gao; Jiaxin Ding; Luoyi Fu; Xinbing Wang; Chenghu Zhou; |
| 499 | Algorithmic Fairness in Distribution of Resources and Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By examining recent advancements and key questions in computational social choice, I highlight challenges and prospects in designing fair systems in collective decision-making that are scalable, adaptable to intricate environments, and are aligned with complex and diverse human preferences. |
Hadi Hosseini; |
| 500 | CLR-Face: Conditional Latent Refinement for Blind Face Restoration Using Score-Based Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a diffusion-based-prior inside a VQGAN architecture that focuses on learning the distribution over uncorrupted latent embeddings. |
Maitreya Suin; Rama Chellappa; |
This table only includes 500 papers selected by our daily digest algorithm. To continue with the full list (~1,000 papers), please visit Paper Digest: IJCAI-2024 (Full List).