Most Influential ECCV Papers (2023-01)
The European Conference on Computer Vision (ECCV) is one of the top computer vision conferences in the world. Paper Digest Team analyzes all papers published on ECCV in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2023-01)
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TABLE 1: Most Influential ECCV Papers (2023-01)
| Year | Rank | Paper | Author(s) |
|---|---|---|---|
| 2022 | 1 | ByteTrack: Multi-Object Tracking By Associating Every Detection Box IF:4 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating almost every detection box instead of only the high score ones. |
YIFU ZHANG et. al. |
| 2022 | 2 | TensoRF: Tensorial Radiance Fields IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present TensoRF, a novel approach to model and reconstruct radiance fields. |
Anpei Chen; Zexiang Xu; Andreas Geiger; Jingyi Yu; Hao Su; |
| 2022 | 3 | MOTR: End-to-End Multiple-Object Tracking with TRansformer IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MOTR, which extends DETR \cite{carion2020detr} and introduces “track query” to model the tracked instances in the entire video. |
FANGAO ZENG et. al. |
| 2022 | 4 | SLIP: Self-Supervision Meets Language-Image Pre-training IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore whether self-supervised learning can aid in the use of language supervision for visual representation learning with Vision Transformers. |
Norman Mu; Alexander Kirillov; David Wagner; Saining Xie; |
| 2022 | 5 | Exploring Plain Vision Transformer Backbones for Object Detection IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. |
Yanghao Li; Hanzi Mao; Ross Girshick; Kaiming He; |
| 2022 | 6 | BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images Via Spatiotemporal Transformers IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. |
ZHIQI LI et. al. |
| 2022 | 7 | VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Current methods rely heavily on training to a specific domain (e.g., only faces), manual work or algorithm tuning to latent vector discovery, and manual effort in mask selection to alter only a part of an image. We address all of these usability constraints while producing images of high visual and semantic quality through a unique combination of OpenAI’s CLIP (Radford et al., 2021), VQGAN (Esser et al., 2021), and a generation augmentation strategy to produce VQGAN-CLIP. |
KATHERINE CROWSON et. al. |
| 2022 | 8 | Visual Prompt Tuning IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces Visual Prompt Tuning (VPT) as an efficient and effective alternative to full fine-tuning for large-scale Transformer models in vision. |
MENGLIN JIA et. al. |
| 2022 | 9 | Make-a-Scene: Scene-Based Text-to-Image Generation with Human Priors IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: While these methods have incrementally improved the generated image fidelity and text relevancy, several pivotal gaps remain unanswered, limiting applicability and quality. We propose a novel text-to-image method that addresses these gaps by (i) enabling a simple control mechanism complementary to text in the form of a scene, (ii) introducing elements that substantially improve the tokenization process by employing domain-specific knowledge over key image regions (faces and salient objects), and (iii) adapting classifier-free guidance for the transformer use case. |
ORAN GAFNI et. al. |
| 2022 | 10 | Detecting Twenty-Thousand Classes Using Image-Level Supervision IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Detic, which simply trains the classifiers of a detector on image classification data and thus expands the vocabulary of detectors to tens of thousands of concepts. |
Xingyi Zhou; Rohit Girdhar; Armand Joulin; Philipp Krä,henbü,hl; Ishan Misra; |
| 2022 | 11 | PETR: Position Embedding Transformation for Multi-View 3D Object Detection IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop position embedding transformation (PETR) for multi-view 3D object detection. |
Yingfei Liu; Tiancai Wang; Xiangyu Zhang; Jian Sun; |
| 2022 | 12 | Simple Baselines for Image Restoration IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple baseline that exceeds the SOTA methods and is computationally efficient. |
Liangyu Chen; Xiaojie Chu; Xiangyu Zhang; Jian Sun; |
| 2022 | 13 | Masked Siamese Networks for Label-Efficient Learning IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. |
MAHMOUD ASSRAN et. al. |
| 2022 | 14 | ActionFormer: Localizing Moments of Actions with Transformers IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we present ActionFormer–a simple yet powerful model to identify actions in time and recognize their categories in a single shot, without using action proposals or relying on pre-defined anchor windows. |
Chen-Lin Zhang; Jianxin Wu; Yin Li; |
| 2022 | 15 | MaxViT: Multi-axis Vision Transformer IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce an efficient and scalable attention model we call multi-axis attention, which consists of two aspects: blocked local and dilated global attention. |
ZHENGZHONG TU et. al. |
| 2020 | 1 | End-to-End Object Detection With Transformers IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new method that views object detection as a direct set prediction. |
NICOLAS CARION et. al. |
| 2020 | 2 | NeRF: Representing Scenes As Neural Radiance Fields For View Synthesis IF:8 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. |
BEN MILDENHALL et. al. |
| 2020 | 3 | Contrastive Multiview Coding IF:8 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study this hypothesis under the framework of multiview contrastive learning, where we learn a representation that aims to maximize mutual information between different views of the same scene but is otherwise compact. |
Yonglong Tian; Dilip Krishnan; Phillip Isola; |
| 2020 | 4 | UNITER: UNiversal Image-TExt Representation Learning IF:7 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. |
YEN-CHUN CHEN et. al. |
| 2020 | 5 | Oscar: Object-Semantics Aligned Pre-training For Vision-Language Tasks IF:7 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: While existing methods simply concatenate image region features and text features as input to the model to be pre-trained and use self-attention to learn image-text semantic alignments in a brute force manner, in this paper, we propose a new learning method Oscar, which uses object tags detected in images as anchor points to significantly ease the learning of alignments. |
XIUJUN LI et. al. |
| 2020 | 6 | RAFT: Recurrent All-Pairs Field Transforms For Optical Flow IF:7 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for estimating optical flow. |
Zachary Teed; Jia Deng; |
| 2020 | 7 | Big Transfer (BiT): General Visual Representation Learning IF:7 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). |
ALEXANDER KOLESNIKOV et. al. |
| 2020 | 8 | Object-Contextual Representations For Semantic Segmentation IF:7 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the semantic segmentation problem with a focus on the context aggregation strategy. |
Yuhui Yuan; Xilin Chen; Jingdong Wang; |
| 2020 | 9 | Single Path One-Shot Neural Architecture Search With Uniform Sampling IF:7 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work propose a Single Path One-Shot model to address the challenge in the training. |
ZICHAO GUO et. al. |
| 2020 | 10 | Rethinking Few-shot Image Classification: A Good Embedding Is All You Need? IF:6 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that a simple baseline: learning a supervised or self-supervised representation on the meta-training set, followed by training a linear classifier on top of this representation, outperforms state-of-the-art few-shot learning methods. |
Yonglong Tian; Yue Wang; Dilip Krishnan; Joshua B. Tenenbaum; Phillip Isola; |
| 2020 | 11 | Tracking Objects As Points IF:6 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a simultaneous detection and tracking algorithm that is simpler, faster, and more accurate than the state of the art. |
Xingyi Zhou; Vladlen Koltun; Philipp Krähenbühl; |
| 2020 | 12 | Contrastive Learning For Unpaired Image-to-Image Translation IF:6 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a straightforward method for doing so — maximizing mutual information between the two, using a framework based on contrastive learning. |
Taesung Park Alexei A. Efros Richard Zhang Jun-Yan Zhu; |
| 2020 | 13 | Convolutional Occupancy Networks IF:6 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. |
Songyou Peng; Michael Niemeyer; Lars Mescheder; Marc Pollefeys; Andreas Geiger; |
| 2020 | 14 | Square Attack: A Query-efficient Black-box Adversarial Attack Via Random Search IF:6 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the Square Attack, a score-based black-box $l_2$- and $l_\infty$- adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. |
Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion; Matthias Hein; |
| 2020 | 15 | In-Domain GAN Inversion For Real Image Editing IF:6 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing. |
Jiapeng Zhu; Yujun Shen; Deli Zhao; Bolei Zhou; |
| 2018 | 1 | Encoder-Decoder With Atrous Separable Convolution For Semantic Image Segmentation IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to combine the advantages from both methods. |
Liang-Chieh Chen; Yukun Zhu; George Papandreou; Florian Schroff; Hartwig Adam; |
| 2018 | 2 | CBAM: Convolutional Block Attention Module IF:8 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Convolutional Block Attention Module (CBAM), a simple and effective attention module that can be integrated with any feed-forward convolutional neural networks. |
Sanghyun Woo; Jongchan Park; Joon-Young Lee; In So Kweon; |
| 2018 | 3 | ShuffleNet V2: Practical Guidelines For Efficient CNN Architecture Design IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Taking these factors into account, this work proposes practical guidelines for efficient network de- sign. |
Ningning Ma; Xiangyu Zhang; Hai-Tao Zheng; Jian Sun; |
| 2018 | 4 | Image Super-Resolution Using Very Deep Residual Channel Attention Networks IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve these problems, we propose the very deep residual channel attention networks (RCAN). |
YULUN ZHANG et. al. |
| 2018 | 5 | CornerNet: Detecting Objects As Paired Keypoints IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. |
Hei Law; Jia Deng; |
| 2018 | 6 | Group Normalization IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Group Normalization (GN) as a simple alternative to BN. |
Yuxin Wu; Kaiming He; |
| 2018 | 7 | Multimodal Unsupervised Image-to-image Translation IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. |
Xun Huang; Ming-Yu Liu; Serge Belongie; Jan Kautz; |
| 2018 | 8 | Deep Clustering For Unsupervised Learning Of Visual Features IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. |
Mathilde Caron; Piotr Bojanowski; Armand Joulin; Matthijs Douze; |
| 2018 | 9 | Progressive Neural Architecture Search IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. |
CHENXI LIU et. al. |
| 2018 | 10 | Image Inpainting For Irregular Holes Using Partial Convolutions IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to use partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. |
GUILIN LIU et. al. |
| 2018 | 11 | BiSeNet: Bilateral Segmentation Network For Real-time Semantic Segmentation IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). |
CHANGQIAN YU et. al. |
| 2018 | 12 | Simple Baselines For Human Pose Estimation And Tracking IF:9 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work provides simple and effective baseline methods. |
Bin Xiao; Haiping Wu; Yichen Wei; |
| 2018 | 13 | Exploring The Limits Of Weakly Supervised Pretraining IF:8 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images. |
DHRUV MAHAJAN et. al. |
| 2018 | 14 | AMC: AutoML For Model Compression And Acceleration On Mobile Devices IF:8 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose AutoML for Model Compression (AMC) which leverages reinforcement learning to efficiently sample the design space and can improve the model compression quality. |
YIHUI HE et. al. |
| 2018 | 15 | ICNet For Real-Time Semantic Segmentation On High-Resolution Images IF:8 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We focus on the challenging task of real-time semantic segmentation in this paper. |
Hengshuang Zhao; Xiaojuan Qi; Xiaoyong Shen; Jianping Shi; Jiaya Jia; |