Paper Digest: ICCV 2015 Highlights

December 12, 2015October 6, 2019 admin

The International Conference on Computer Vision (ICCV) is one of the top computer vision conferences in the world. In 2015, it is to be held in Santiago, Chile

To help AI 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 / summaries to quickly get the main idea of each paper.

We thank all authors for writing these interesting papers, and readers for reading our digests. If you do not want to miss any interesting AI paper, you are welcome to sign up our free paper digest service to get new paper updates customized to your own interests on a daily basis.

Paper Digest Team
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TABLE 1: ICCV 2015 Papers

	Title	Authors	Highlight
1	Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images	Mateusz Malinowski, Marcus Rohrbach, Mario Fritz	By combining latest advances in image representation and natural language processing, we propose Neural-Image-QA, an end-to-end formulation to this problem for which all parts are trained jointly.
2	Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing	Hamid Izadinia, Fereshteh Sadeghi, Santosh K. Divvala, Hannaneh Hajishirzi, Yejin Choi, Ali Farhadi	We introduce Segment-Phrase Table (SPT), a large collection of bijective associations between textual phrases and their corresponding segmentations.
3	Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books	Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler	To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book.
4	Learning Query and Image Similarities With Ranking Canonical Correlation Analysis	Ting Yao, Tao Mei, Chong-Wah Ngo	We demonstrate in this paper that the above two limitations can be well mitigated by jointly exploring subspace learning and the use of click-through data.
5	Learning to See by Moving	Pulkit Agrawal, Joao Carreira, Jitendra Malik	Drawing inspiration from this observation, in this work we investigated if the awareness of egomotion(i.e. self motion) can be used as a supervisory signal for feature learning.
6	Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features	Haoyu Ren, Ze-Nian Li	In this paper, we propose a high-accuracy object detector based on co-occurrence features.
7	Mining And-Or Graphs for Graph Matching and Object Discovery	Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu	Given a set of attributed relational graphs (ARGs), we propose to use a hierarchical And-Or Graph (AoG) to model the pattern of maximal-size common subgraphs embedded in the ARGs, and we develop a general method to mine the AoG model from the unlabeled ARGs.
8	Pose Induction for Novel Object Categories	Shubham Tulsiani, Joao Carreira, Jitendra Malik	We present a generalized classifier that can reliably induce pose given a single instance of a novel category.
9	Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning	Yuhui Quan, Yan Huang, Hui Ji	To overcome these obstacles, we proposed a structured tensor dictionary learning method for sparse coding, which learns a dictionary structured with orthogonality and separability.
10	Convolutional Channel Features	Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li	In this paper, we revisit two widely used approaches in computer vision, namely filtered channel features and Convolutional Neural Networks (CNN), and absorb merits from both by proposing an integrated method called Convolutional Channel Features (CCF).
11	Local Convolutional Features With Unsupervised Training for Image Retrieval	Mattis Paulin, Matthijs Douze, Zaid Harchaoui, Julien Mairal, Florent Perronin, Cordelia Schmid	We present a comparison framework to benchmark current deep convolutional approaches along with Patch-CKN for both patch and image retrieval, including our novel “RomePatches” dataset.
12	RIDE: Reversal Invariant Descriptor Enhancement	Lingxi Xie, Jingdong Wang, Weiyao Lin, Bo Zhang, Qi Tian	In this paper, we present RIDE (Reversal Invariant Descriptor Enhancement) for fine-grained object recognition.
13	Discrete Tabu Search for Graph Matching	Kamil Adamczewski, Yumin Suh, Kyoung Mu Lee	In this paper, we propose a novel graph matching algorithm based on tabu search.
14	Discriminative Learning of Deep Convolutional Feature Point Descriptors	Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, Francesc Moreno-Noguer	In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non-)corresponding patches.
15	Amodal Completion and Size Constancy in Natural Scenes	Abhishek Kar, Shubham Tulsiani, Joao Carreira, Jitendra Malik	Here we propose to tackle these issues by building upon advances in object recognition and using recently created large-scale datasets.
16	Learning Where to Position Parts in 3D	Marco Pedersoli, Tinne Tuytelaars	In this paper we propose a new method for the detection and pose estimation of 3D objects, that does not use any 3D CAD model or other 3D information.
17	Query Adaptive Similarity Measure for RGB-D Object Recognition	Yanhua Cheng, Rui Cai, Chi Zhang, Zhiwei Li, Xin Zhao, Kaiqi Huang, Yong Rui	This paper studies the problem of improving the top-1 accuracy of RGB-D object recognition.
18	Listening With Your Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines	Chao Sui, Mohammed Bennamoun, Roberto Togneri	This paper presents a novel feature learning method for visual speech recognition using Deep Boltzmann Machines (DBM).
19	Cluster-Based Point Set Saliency	Flora Ponjou Tasse, Jiri Kosinka, Neil Dodgson	We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information.
20	A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms	Shida Beigpour, Andreas Kolb, Sven Kunz	In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information.
21	PatchMatch-Based Automatic Lattice Detection for Near-Regular Textures	Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N. Do, Eli Shechtman, Nathan Carr	In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images.
22	A Data-Driven Metric for Comprehensive Evaluation of Saliency Models	Jia Li, Changqun Xia, Yafei Song, Shu Fang, Xiaowu Chen	To address this problem, we propose a data-driven metric for comprehensive evaluation of saliency models.
23	A Matrix Decomposition Perspective to Multiple Graph Matching	Junchi Yan, Hongteng Xu, Hongyuan Zha, Xiaokang Yang, Huanxi Liu, Stephen Chu	Our method aims to extract the common inliers and their synchronized permutations from disordered weighted graphs in the presence of deformation and outliers.
24	Fast and Effective L0 Gradient Minimization by Region Fusion	Rang M. H. Nguyen, Michael S. Brown	In this paper, we present a new method to perform L_0 gradient minimization that is fast and effective.
25	Generic Promotion of Diffusion-Based Salient Object Detection	Peng Jiang, Nuno Vasconcelos, Jingliang Peng	In this work, we propose a generic scheme to promote any diffusion-based salient object detection algorithm by original ways to re-synthesize the diffusion matrix and construct the seed vector.
26	Nighttime Haze Removal With Glow and Multiple Light Colors	Yu Li, Robby T. Tan, Michael S. Brown	To address these effects, we introduce a new nighttime haze model that accounts for the varying light sources and their glow.
27	Conformal and Low-Rank Sparse Representation for Image Restoration	Jianwei Li, Xiaowu Chen, Dongqing Zou, Bo Gao, Wei Teng	In this paper, we propose a novel sparse representation approach called conformal and low-rank sparse representation (CLRSR) for image restoration problems.
28	Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising	Jun Xu, Lei Zhang, Wangmeng Zuo, David Zhang, Xiangchu Feng	In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising.
29	Automatic Thumbnail Generation Based on Visual Representativeness and Foreground Recognizability	Jingwei Huang, Huarong Chen, Bin Wang, Stephen Lin	We present an automatic thumbnail generation technique based on two essential considerations: how well they visually represent the original photograph, and how well the foreground can be recognized after the cropping and downsizing steps of thumbnailing. We propose a set of image features for modeling these two considerations of thumbnails, and learn how to balance their relative effects on thumbnail generation through training on image pairs composed of photographs and their corresponding thumbnails created by an expert photographer.
30	SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks	Xun Huang, Chengyao Shen, Xavier Boix, Qi Zhao	This paper presents a focused study to narrow the semantic gap with an architecture based on Deep Neural Network (DNN).
31	A Novel Sparsity Measure for Tensor Recovery	Qian Zhao, Deyu Meng, Xu Kong, Qi Xie, Wenfei Cao, Yao Wang, Zongben Xu	In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor.
32	Oriented Object Proposals	Shengfeng He, Rynson W.H. Lau	In this paper, we propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object.
33	Learning Nonlinear Spectral Filters for Color Image Reconstruction	Michael Moeller, Julia Diebold, Guy Gilboa, Daniel Cremers	This paper presents the idea of learning optimal filters for color image reconstruction based on a novel concept of nonlinear spectral image decompositions recently proposed by Guy Gilboa.
34	Beyond White: Ground Truth Colors for Color Constancy Correction	Dongliang Cheng, Brian Price, Scott Cohen, Michael S. Brown	In this paper, we describe how to overcome this limitation.
35	RGB-Guided Hyperspectral Image Upsampling	Hyeokhyen Kwon, Yu-Wing Tai	In this paper, we present an algorithm to enhance and upsample the resolution of hyperspectral images.
36	Projection Onto the Manifold of Elongated Structures for Accurate Extraction	Amos Sironi, Vincent Lepetit, Pascal Fua	We solve this problem by projecting patches of the score map to their nearest neighbors in a set of ground truth training patches.
37	Naive Bayes Super-Resolution Forest	Jordi Salvador, Eduardo Perez-Pellitero	This paper presents a fast, high-performance method for super resolution with external learning.
38	POP Image Fusion – Derivative Domain Image Fusion Without Reintegration	Graham D. Finlayson, Alex E. Hayes	In this paper we avoid these hallucinated details by avoiding the reintegration step.
39	Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising	Ying Fu, Antony Lam, Imari Sato, Yoichi Sato	In this paper, we propose an effective model for hyperspectral image (HSI) denoising that considers underlying characteristics of HSIs: sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space.
40	Fully Connected Guided Image Filtering	Longquan Dai, Mengke Yuan, Feihu Zhang, Xiaopeng Zhang	This paper presents a linear time fully connected guided filter by introducing the minimum spanning tree (MST) to the guided filter (GF).
41	Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing	Feihu Zhang, Longquan Dai, Shiming Xiang, Xiaopeng Zhang	In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel, which enables the filter to smooth out high-contrast details and textures while preserving major image structures very well.
42	Deep Networks for Image Super-Resolution With Sparse Prior	Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, Thomas Huang	In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results.
43	Convolutional Color Constancy	Jonathan T. Barron	In contrast, in this paper we reformulate the problem of color constancy as a 2D spatial localization task in a log-chrominance space, thereby allowing us to apply techniques from object detection and structured prediction to the color constancy problem.
44	Learning Ordinal Relationships for Mid-Level Vision	Daniel Zoran, Phillip Isola, Dilip Krishnan, William T. Freeman	We propose a framework that infers mid-level visual properties of an image by learning about ordinal relation- ships.
45	Thin Structure Estimation With Curvature Regularization	Dmitrii Marin, Yuchen Zhong, Maria Drangova, Yuri Boykov	We propose an objective function combining detection likelihoods with a prior minimizing curvature of the center-lines or surfaces.
46	HARF: Hierarchy-Associated Rich Features for Salient Object Detection	Wenbin Zou, Nikos Komodakis	To address such an issue, this paper proposes a novel hierarchy-associated feature construction framework for salient object detection, which is based on integrating elementary features from multi-level regions in a hierarchy.
47	Deep Colorization	Zezhou Cheng, Qingxiong Yang, Bin Sheng	Unlike the previous methods, this paper aims at a high-quality fully-automatic colorization method.
48	Image Matting With KL-Divergence Based Sparse Sampling	Levent Karacan, Aykut Erdem, Erkut Erdem	To alleviate this, in this paper we take an entirely new approach and formulate sampling as a sparse subset selection problem where we propose to pick a small set of candidate samples that best explains the unknown pixels.
49	Intrinsic Decomposition of Image Sequences From Local Temporal Variations	Pierre-Yves Laffont, Jean-Charles Bazin	We present a method for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers.
50	Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising	Weisheng Dong, Guangyu Li, Guangming Shi, Xin Li, Yi Ma	In this work, we propose a novel low-rank tensor approximation framework with Laplacian Scale Mixture (LSM) modeling for multi-frame image denoising.
51	Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding	Yongbo Li, Weisheng Dong, Guangming Shi, Xuemei Xie	In this paper, we propose to develop a hybrid approach toward SR by combining those two lines of ideas.
52	Improving Image Restoration With Soft-Rounding	Xing Mei, Honggang Qi, Bao-Gang Hu, Siwei Lyu	In this work, we describe an effective and efficient approach to incorporate the knowledge of distinct pixel values of the pristine images into the general regularized least squares restoration framework.
53	See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG	Wei-Chen Chiu, Mario Fritz	We present our implementation of [?]
54	An Efficient Statistical Method for Image Noise Level Estimation	Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng	In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise.
55	Contour Detection and Characterization for Asynchronous Event Sensors	Francisco Barranco, Ching L. Teo, Cornelia Fermuller, Yiannis Aloimonos	This paper presents an approach to learn the location of contours and their border ownership using Structured Random Forests on event-based features that encode motion, timing, texture, and spatial orientations.
56	Class-Specific Image Deblurring	Saeed Anwar, Cong Phuoc Huynh, Fatih Porikli	In this paper, we explore the potential of a class-specific image prior for recovering spatial frequencies attenuated by the blurring process.
57	High-for-Low and Low-for-High: Efficient Boundary Detection From Deep Object Features and its Applications to High-Level Vision	Gedas Bertasius, Jianbo Shi, Lorenzo Torresani	Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network.
58	Variational Depth Superresolution Using Example-Based Edge Representations	David Ferstl, Matthias Ruther, Horst Bischof	In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel.
59	Conditioned Regression Models for Non-Blind Single Image Super-Resolution	Gernot Riegler, Samuel Schulter, Matthias Ruther, Horst Bischof	In this paper, we loosen this restrictive constraint and propose conditioned regression models (including convolutional neural networks and random forests) that can effectively exploit the additional kernel information during both, training and inference.
60	Video Super-Resolution via Deep Draft-Ensemble Learning	Renjie Liao, Xin Tao, Ruiyu Li, Ziyang Ma, Jiaya Jia	We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution.
61	Pan-Sharpening With a Hyper-Laplacian Penalty	Yiyong Jiang, Xinghao Ding, Delu Zeng, Yue Huang, John Paisley	We present a method for pan-sharpening in which a sparsity-promoting objective function preserves both spatial and spectral content, and is efficient to optimize.
62	Video Restoration Against Yin-Yang Phasing	Xiaolin Wu, Zhenhao Li, Xiaowei Deng	In this paper, we investigate the problem and propose a video restoration technique to suppress YYP artifacts and retain temporal consistency of objects appearance via inter-frame, spatially-adaptive, optimal tone mapping.
63	Rolling Shutter Super-Resolution	Abhijith Punnappurath, Vijay Rengarajan, A.N. Rajagopalan	In this paper, we study the hitherto unexplored topic of multi-image SR in CMOS cameras.
64	Learning Large-Scale Automatic Image Colorization	Aditya Deshpande, Jason Rock, David Forsyth	We describe an automated method for image colorization that learns to colorize from examples.
65	Compression Artifacts Reduction by a Deep Convolutional Network	Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang	Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restores sharpened images that are accompanied with ringing effects.
66	Multiple-Hypothesis Affine Region Estimation With Anisotropic LoG Filters	Takahiro Hasegawa, Mitsuru Ambai, Kohta Ishikawa, Gou Koutaki, Yuji Yamauchi, Takayoshi Yamashita, Hironobu Fujiyoshi	We propose a method for estimating multiple-hypothesis affine regions from a keypoint by using an anisotropic Laplacian-of-Gaussian (LoG) filter.
67	A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection	Dingwen Zhang, Deyu Meng, Chao Li, Lu Jiang, Qian Zhao, Junwei Han	To alleviate this problem, we propose a novel framework for this task, by naturally reformulating it as a multiple-instance learning (MIL) problem and further integrating it into a self-paced learning (SPL) regime.
68	External Patch Prior Guided Internal Clustering for Image Denoising	Fei Chen, Lei Zhang, Huimin Yu	In this paper, we propose to exploit image external patch prior and internal self-similarity prior jointly, and develop an external patch prior guided internal clustering algorithm for image denoising.
69	Self-Calibration of Optical Lenses	Michael Hirsch, Bernhard Scholkopf	We propose a method that enables the self-calibration of lenses from a natural image, or a set of such images.
70	Illumination Robust Color Naming via Label Propagation	Yuanliu liu, Zejian Yuan, Badong Chen, Jianru Xue, Nanning Zheng	In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically. For evaluation we collect three datasets of images under noticeable highlights and shadows.
71	Unsupervised Cross-Modal Synthesis of Subject-Specific Scans	Raviteja Vemulapalli, Hien Van Nguyen, Shaohua Kevin Zhou	Hence, to address this issue, we propose a general unsupervised cross-modal medical image synthesis approach that works without paired training data.
72	Learning to Boost Filamentary Structure Segmentation	Lin Gu, Li Cheng	Focusing on this issue, this paper proposes an iterative two-step learning-based approach to boost the performance based on a base segmenter arbitrarily chosen from a number of existing segmenters: We start with an initial partial segmentation where the filamentary structure obtained is of high confidence based on this existing segmenter.
73	Weakly-Supervised Structured Output Learning With Flexible and Latent Graphs Using High-Order Loss Functions	Gustavo Carneiro, Tingying Peng, Christine Bayer, Nassir Navab	We introduce two new structured output models that use a latent graph, which is flexible in terms of the number of nodes and structure, where the training process minimises a high-order loss function using a weakly annotated training set.
74	Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation	Toufiq Parag, Dan C. Ciresan, Alessandro Giusti	This study proposes a novel classifier training algorithm for EM segmentation aimed to reduce the amount of manual effort demanded by the groundtruth annotation and error refinement tasks.
75	On Statistical Analysis of Neuroimages With Imperfect Registration	Won Hwa Kim, Sathya N. Ravi, Sterling C. Johnson, Ozioma C. Okonkwo, Vikas Singh	In this paper, we derive a novel algorithm which offers immunity to local errors in the underlying deformation field obtained from registration procedures. We present a set of results on synthetic and real brain images where we achieve robust statistical analysis even in the presence of substantial deformation errors; here, standard analysis procedures significantly under-perform and fail to identify the true signal.
76	Convex Optimization With Abstract Linear Operators	Steven Diamond, Stephen Boyd	We introduce a convex optimization modeling framework that transforms a convex optimization problem expressed in a form natural and convenient for the user into an equivalent cone program in a way that preserves fast linear transforms in the original problem.
77	Building Dynamic Cloud Maps From the Ground Up	Calvin Murdock, Nathan Jacobs, Robert Pless	We demonstrate how this imagery can be constructed “from the ground up” without requiring expensive geo-stationary satellites.
78	A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online	David Joseph Tan, Federico Tombari, Slobodan Ilic, Nassir Navab	This paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time.
79	Realtime Edge-Based Visual Odometry for a Monocular Camera	Juan Jose Tarrio, Sol Pedre	In this work we present a novel algorithm for realtime visual odometry for a monocular camera.
80	Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning	Lap-Fai Yu, Noah Duncan, Sai-Kit Yeung	In this paper we introduce a novel approach to reason about liquid containability – the affordance of containing liquid.
81	On Linear Structure From Motion for Light Field Cameras	Ole Johannsen, Antonin Sulc, Bastian Goldluecke	We present a novel approach to relative pose estimation which is tailored to 4D light field cameras.
82	3D Object Reconstruction From Hand-Object Interactions	Dimitrios Tzionas, Juergen Gall	In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.
83	Minimal Solvers for 3D Geometry From Satellite Imagery	Enliang Zheng, Ke Wang, Enrique Dunn, Jan-Michael Frahm	We propose two novel minimal solvers which advance the state of the art in satellite imagery processing.
84	An Efficient Minimal Solution for Multi-Camera Motion	Jonathan Ventura, Clemens Arth, Vincent Lepetit	We propose an efficient method for estimating the motion of a multi-camera rig from a minimal set of feature correspondences.
85	Learning Shape, Motion and Elastic Models in Force Space	Antonio Agudo, Francesc Moreno-Noguer	In this paper, we address the problem of simultaneously recovering the 3D shape and pose of a deformable and potentially elastic object from 2D motion.
86	A Versatile Scene Model With Differentiable Visibility Applied to Generative Pose Estimation	Helge Rhodin, Nadia Robertini, Christian Richardt, Hans-Peter Seidel, Christian Theobalt	We present a new scene representation that enables an analytically differentiable closed-form formulation of surface visibility.
87	Semantic Pose Using Deep Networks Trained on Synthetic RGB-D	Jeremie Papon, Markus Schoeler	In this work we address the problem of indoor scene understanding from RGB-D images.
88	Exploiting High Level Scene Cues in Stereo Reconstruction	Simon Hadfield, Richard Bowden	We present a novel approach to 3D reconstruction which is inspired by the human visual system.
89	Point Triangulation Through Polyhedron Collapse Using the l[?] Norm	Simon Donne, Bart Goossens, Wilfried Philips	In this paper we present a novel method for L-infinity triangulation that minimizes the L-infinity norm of the L-infinity reprojection errors: this apparently small difference leads to a much faster but equally accurate solution which is related to the MLE under the assumption of uniform noise.
90	Optimizing the Viewing Graph for Structure-From-Motion	Chris Sweeney, Torsten Sattler, Tobias Hollerer, Matthew Turk, Marc Pollefeys	In this paper, we take a fundamentally different approach to SfM and instead focus on improving the quality of the viewing graph before applying SfM.
91	Intrinsic Scene Decomposition From RGB-D images	Mohammed Hachama, Bernard Ghanem, Peter Wonka	In this paper, we address the problem of computing an intrinsic decomposition of the colors of a surface into an albedo and a shading term.
92	3D Hand Pose Estimation Using Randomized Decision Forest With Segmentation Index Points	Peiyi Li, Haibin Ling, Xi Li, Chunyuan Liao	In this paper, we propose a real-time 3D hand pose estimation algorithm using the randomized decision forest framework.
93	Accurate Camera Calibration Robust to Defocus Using a Smartphone	Hyowon Ha, Yunsu Bok, Kyungdon Joo, Jiyoung Jung, In So Kweon	We propose a novel camera calibration method for defocused images using a smartphone under the assumption that the defocus blur is modeled as a convolution of a sharp image with a Gaussian point spread function (PSF).
94	High Quality Structure From Small Motion for Rolling Shutter Cameras	Sunghoon Im, Hyowon Ha, Gyeongmin Choe, Hae-Gon Jeon, Kyungdon Joo, In So Kweon	We present a practical 3D reconstruction method to obtain a high-quality dense depth map from narrow-baseline image sequences captured by commercial digital cameras, such as DSLRs or mobile phones.
95	Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction	Paulo F. U. Gotardo, Tomas Simon, Yaser Sheikh, Iain Matthews	This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction.
96	Blur-Aware Disparity Estimation From Defocus Stereo Images	Ching-Hui Chen, Hui Zhou, Timo Ahonen	We propose a blur-aware disparity estimation method that is robust to the mismatch of focus in stereo images.
97	Global Structure-From-Motion by Similarity Averaging	Zhaopeng Cui, Ping Tan	We propose to compute a sparse depth image at each camera to solve both problems.
98	Massively Parallel Multiview Stereopsis by Surface Normal Diffusion	Silvano Galliani, Katrin Lasinger, Konrad Schindler	We present a new, massively parallel method for high-quality multiview matching.
99	Variational PatchMatch MultiView Reconstruction and Refinement	Philipp Heise, Brian Jensen, Sebastian Klose, Alois Knoll	In this work we propose a novel approach to the problem of multi-view stereo reconstruction.
100	As-Rigid-As-Possible Volumetric Shape-From-Template	Shaifali Parashar, Daniel Pizarro, Adrien Bartoli, Toby Collins	We present strategies to find an initial solution based on thin-shell SfT and volume propagation.
101	General Dynamic Scene Reconstruction From Multiple View Video	Armin Mustafa, Hansung Kim, Jean-Yves Guillemaut, Adrian Hilton	This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination.
102	The Joint Image Handbook	Matthew Trager, Martial Hebert, Jean Ponce	We revisit in this paper the geometric and algebraic properties of the joint image, and address fundamental questions such as how many and which multilinearities are necessary and/or sufficient to determine camera geometry and/or image correspondences.
103	Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction From RGB Video	Rui Yu, Chris Russell, Neill D. F. Campbell, Lourdes Agapito	In this paper we tackle the problem of capturing the dense, detailed 3D geometry of generic, complex non-rigid meshes using a single RGB-only commodity video camera and a direct approach.
104	Single Image Pop-Up From Discriminatively Learned Parts	Menglong Zhu, Xiaowei Zhou, Kostas Daniilidis	We introduce a new approach for estimating a fine grained 3D shape and continuous pose of an object from a single image.
105	Learning Informative Edge Maps for Indoor Scene Layout Prediction	Arun Mallya, Svetlana Lazebnik	In this paper, we introduce new edge-based features for the task of recovering the 3D layout of an indoor scene from a single image.
106	Multi-View Convolutional Neural Networks for 3D Shape Recognition	Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller	We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images.
107	Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images	Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother	We propose an approach that “learns to compare”, while taking these difficulties into account.
108	3D Surface Profilometry Using Phase Shifting of De Bruijn Pattern	Matea Donlic, Tomislav Petkovic, Tomislav Pribanic	A novel structured light method for color 3D surface profilometry is proposed.
109	A Deep Visual Correspondence Embedding Model for Stereo Matching Costs	Zhuoyuan Chen, Xun Sun, Liang Wang, Yinan Yu, Chang Huang	This paper presents a data-driven matching cost for stereo matching.
110	Learning Concept Embeddings With Combined Human-Machine Expertise	Michael Wilber, Iljung S. Kwak, David Kriegman, Serge Belongie	This paper presents our work on “SNaCK,” a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels.
111	Deep Multi-Patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation	Xin Lu, Zhe Lin, Xiaohui Shen, Radomir Mech, James Z. Wang	We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image.
112	Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection	Xiaodan Liang, Si Liu, Yunchao Wei, Luoqi Liu, Liang Lin, Shuicheng Yan	Inspired by these observations, we propose a computational model for weakly-supervised object detection, based on prior knowledge modelling, exemplar learning and learning with video contexts.
113	Improving Image Classification With Location Context	Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, Lubomir Bourdev	In this work, we tackle the problem of performing image classification with location context, in which we are given the GPS coordinates for images in both the train and test phases. To evaluate our model and to help promote research in this area, we identify a set of location-sensitive concepts and annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has GPS coordinates with these concepts, which we make publicly available.
114	HICO: A Benchmark for Recognizing Human-Object Interactions in Images	Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, Jia Deng	We introduce a new benchmark “Humans Interacting with Common Objects” (HICO) for recognizing human-object interactions (HOI).
115	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification	Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun	In this work, we study rectifier neural networks for image classification from two aspects.
116	Continuous Pose Estimation With a Spatial Ensemble of Fisher Regressors	Michele Fenzi, Laura Leal-Taixe, Jorn Ostermann, Tinne Tuytelaars	In this paper, we treat the problem of continuous pose estimation for object categories as a regression problem on the basis of only 2D training information.
117	Adaptive Hashing for Fast Similarity Search	Fatih Cakir, Stan Sclaroff	To overcome these challenges, we propose an online learning algorithm based on stochastic gradient descent in which the hash functions are updated iteratively with streaming data.
118	Single Image 3D Without a Single 3D Image	David F. Fouhey, Wajahat Hussain, Abhinav Gupta, Martial Hebert	In this paper, we show that one can learn a mapping from appearance to 3D properties without ever seeing a single explicit 3D label.
119	Cross-Domain Image Retrieval With a Dual Attribute-Aware Ranking Network	Junshi Huang, Rogerio S. Feris, Qiang Chen, Shuicheng Yan	We address the problem of cross-domain image retrieval, considering the following practical application: given a user photo depicting a clothing image, our goal is to retrieve the same or attribute-similar clothing items from online shopping stores.
120	Attribute-Graph: A Graph Based Approach to Image Ranking	Nikita Prabhu, R. Venkatesh Babu	We propose a novel image representation, termed Attribute-Graph, to rank images by their semantic similarity to a given query image.
121	Contextual Action Recognition With R*CNN	Georgia Gkioxari, Ross Girshick, Jitendra Malik	In this work, we exploit the simple observation that actions are accompanied by contextual cues to build a strong action recognition system.
122	What Makes an Object Memorable?	Rachit Dubey, Joshua Peterson, Aditya Khosla, Ming-Hsuan Yang, Bernard Ghanem	In this paper, we provide the first attempt to answer the question: what exactly is remembered about an image?
123	kNN Hashing With Factorized Neighborhood Representation	Kun Ding, Chunlei Huo, Bin Fan, Chunhong Pan	Based on the observation that retrieval precision is highly related to the kNN classification accuracy, this paper proposes a novel kNN-based supervised hashing method, which learns hash functions by directly maximizing the kNN accuracy of the Hamming-embedded training data.
124	Multi-View Complementary Hash Tables for Nearest Neighbor Search	Xianglong Liu, Lei Huang, Cheng Deng, Jiwen Lu, Bo Lang	For single multi-view table, using exemplar based feature fusion, we approximate the inherent data similarities with a low-rank matrix, and learn discriminative hash functions in an efficient way.
125	Scalable Person Re-Identification: A Benchmark	Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, Qi Tian	As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor.
126	MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition	Anran Wang, Jianfei Cai, Jiwen Lu, Tat-Jen Cham	Motivated by the intuition that different modalities should contain not only some modal-specific patterns but also some shared common patterns, we propose a multi-modal feature learning framework for RGB-D object recognition.
127	Object Detection via a Multi-Region and Semantic Segmentation-Aware CNN Model	Spyros Gidaris, Nikos Komodakis	We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features.
128	Neural Activation Constellations: Unsupervised Part Model Discovery With Convolutional Networks	Marcel Simon, Erik Rodner	We present an approach that is able to learn part models in a completely unsupervised manner, without part annotations and even without given bounding boxes during learning.
129	Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection	David Novotny, Jiri Matas	A novel efficient method for extraction of object proposals is introduced.
130	Probabilistic Label Relation Graphs With Ising Models	Nan Ding, Jia Deng, Kevin P. Murphy, Hartmut Neven	In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is “sort of” furry, but not to the same degree as a grizzly bear).
131	Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD	Hyo Jin Kim, Enrique Dunn, Jan-Michael Frahm	We address the problem of recognizing a place depicted in a query image by using a large database of geo-tagged images at a city-scale.
132	Task-Driven Feature Pooling for Image Classification	Guo-Sen Xie, Xu-Yao Zhang, Xiangbo Shu, Shuicheng Yan, Cheng-Lin Liu	In this paper, we propose a novel task-driven pooling (TDP) model to directly learn the pooled representation from data in a discriminative manner.
133	Cutting Edge: Soft Correspondences in Multimodal Scene Parsing	Sarah Taghavi Namin, Mohammad Najafi, Mathieu Salzmann, Lars Petersson	In this paper, we address the problem of data misalignment and label inconsistencies, e.g., due to moving objects, in semantic labeling, which violate the assumption of existing techniques.
134	One Shot Learning via Compositions of Meaningful Patches	Alex Wong, Alan L. Yuille	We propose an unsupervised method for learning a compact dictionary of image patches representing meaningful components of an objects.
135	FASText: Efficient Unconstrained Scene Text Detector	Michal Busta, Lukas Neumann, Jiri Matas	We propose a novel easy-to-implement stroke detector based on an efficient pixel intensity comparison to surrounding pixels.
136	Multi-Scale Recognition With DAG-CNNs	Songfan Yang, Deva Ramanan	We present extensive analysis and demonstrate state-of-the-art classification performance on three standard scene benchmarks (SUN397, MIT67, and Scene15).
137	Relaxed Multiple-Instance SVM With Application to Object Discovery	Xinggang Wang, Zhuotun Zhu, Cong Yao, Xiang Bai	In this paper, we propose a novel method to solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM).
138	Im2Calories: Towards an Automated Mobile Vision Food Diary	Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy	We present CNN-based approaches to these problems, with promising preliminary results.
139	LEWIS: Latent Embeddings for Word Images and their Semantics	Albert Gordo, Jon Almazan, Naila Murray, Florent Perronin	The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images.
140	Per-Sample Kernel Adaptation for Visual Recognition and Grouping	Borislav Antic, Bjorn Ommer	Our goal is, therefore, to adjust the contribution of individual feature dimensions when comparing any two samples and computing their similarity.
141	Fine-Grained Change Detection of Misaligned Scenes With Varied Illuminations	Wei Feng, Fei-Peng Tian, Qian Zhang, Nan Zhang, Liang Wan, Jizhou Sun	This paper proposes a feasible end-to-end approach to this challenging problem. We build three real-world datasets to benchmark fine-grained change detection of misaligned scenes under varied multiple lighting conditions.
142	Aggregating Local Deep Features for Image Retrieval	Artem Babenko, Victor Lempitsky	In this paper we investigate possible ways to aggregate local deep features to produce compact descriptors for image retrieval.
143	Learning Deep Object Detectors From 3D Models	Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko	In a detailed analysis, we use synthetic CAD images to probe the ability of DCNN to learn without these cues, with surprising findings.
144	Harvesting Discriminative Meta Objects With Deep CNN Features for Scene Classification	Ruobing Wu, Baoyuan Wang, Wenping Wang, Yizhou Yu	In this paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification.
145	Scalable Nonlinear Embeddings for Semantic Category-Based Image Retrieval	Gaurav Sharma, Bernt Schiele	We propose a novel algorithm for the task of supervised discriminative distance learning by nonlinearly embedding vectors into a low dimensional Euclidean space.
146	Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis	Jorge Garcia, Niki Martinel, Christian Micheloni, Alfredo Gardel	In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis.
147	Unsupervised Generation of a Viewpoint Annotated Car Dataset From Videos	Nima Sedaghat, Thomas Brox	In this paper,we present an approach that creates a dataset of images annotated with bounding boxes and viewpoint labels in a fully automated manner from videos.
148	Structured Indoor Modeling	Satoshi Ikehata, Hang Yang, Yasutaka Furukawa	This paper presents a novel 3D modeling framework that reconstructs an indoor scene as a structured model from panorama RGBD images.
149	3D Time-Lapse Reconstruction From Internet Photos	Ricardo Martin-Brualla, David Gallup, Steven M. Seitz	3D Time-Lapse Reconstruction From Internet Photos
150	Global, Dense Multiscale Reconstruction for a Billion Points	Benjamin Ummenhofer, Thomas Brox	We present a variational approach for surface reconstruction from a set of oriented points with scale information.
151	On the Visibility of Point Clouds	Sagi Katz, Ayellet Tal	We show that three such properties are sufficient: the sign of the function, monotonicity, and a condition regarding the function’s parameter.
152	Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts	Niloufar Pourian, S. Karthikeyan, B.S. Manjunath	We present a weakly-supervised approach to semantic segmentation.
153	Piecewise Flat Embedding for Image Segmentation	Yizhou Yu, Chaowei Fang, Zicheng Liao	In this paper, we propose a new nonlinear embedding, called piecewise flat embedding, for image segmentation.
154	Semantic Image Segmentation via Deep Parsing Network	Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen-Change Loy, Xiaoou Tang	Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms.
155	Human Parsing With Contextualized Convolutional Neural Network	Xiaodan Liang, Chunyan Xu, Xiaohui Shen, Jianchao Yang, Si Liu, Jinhui Tang, Liang Lin, Shuicheng Yan	In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network.
156	Holistically-Nested Edge Detection	Saining Xie, Zhuowen Tu	We develop a new edge detection algorithm that addresses two critical issues in this long-standing vision problem: (1) holistic image training; and (2) multi-scale feature learning.
157	Minimum Barrier Salient Object Detection at 80 FPS	Jianming Zhang, Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech	We propose a highly efficient, yet powerful, salient object detection method based on the Minimum Barrier Distance (MBD) Transform.
158	Learning Image Representations Tied to Ego-Motion	Dinesh Jayaraman, Kristen Grauman	We propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video.
159	Unsupervised Visual Representation Learning by Context Prediction	Carl Doersch, Abhinav Gupta, Alexei A. Efros	This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation.
160	Webly Supervised Learning of Convolutional Networks	Xinlei Chen, Abhinav Gupta	We present an approach to utilize large amounts of web data for learning CNNs.
161	Fast R-CNN	Ross Girshick	This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection.
162	Bilinear CNN Models for Fine-Grained Visual Recognition	Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji	We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor.
163	Discovering the Spatial Extent of Relative Attributes	Fanyi Xiao, Yong Jae Lee	We present a weakly-supervised approach that discovers the spatial extent of relative attributes, given only pairs of ordered images.
164	Deep Neural Decision Forests	Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulo	To combine these two worlds, we introduce a stochastic and differentiable decision tree model, which steers the representation learning usually conducted in the initial layers of a (deep) convolutional network.
165	Deep Fried Convnets	Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alex Smola, Le Song, Ziyu Wang	In this paper we show how kernel methods, in particular a single Fastfood layer, can be used to replace the fully connected layers in a deep convolutional neural network.
166	Semantic Component Analysis	Calvin Murdock, Fernando De la Torre	To address the issues of previous work, we present a simple but effective method called Semantic Component Analysis (SCA), which provides a decomposition of images into semantic components.
167	Low-Rank Matrix Factorization Under General Mixture Noise Distributions	Xiangyong Cao, Yang Chen, Qian Zhao, Deyu Meng, Yao Wang, Dong Wang, Zongben Xu	To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as Mixture of Exponential Power (MoEP) distributions and proposes a penalized MoEP model by combining the penalized likelihood method with MoEP distributions.
168	Web-Scale Image Clustering Revisited	Yannis Avrithis, Yannis Kalantidis, Evangelos Anagnostopoulos, Ioannis Z. Emiris	Combined with powerful deep learned representations, we achieve clustering of a 100 million image collection on a single machine in less than one hour.
169	Learning Discriminative Reconstructions for Unsupervised Outlier Removal	Yan Xia, Xudong Cao, Fang Wen, Gang Hua, Jian Sun	We study the problem of automatically removing outliers from noisy data, with application for removing outlier images from an image collection.
170	Learning Deconvolution Network for Semantic Segmentation	Hyeonwoo Noh, Seunghoon Hong, Bohyung Han	We propose a novel semantic segmentation algorithm by learning a deep deconvolution network.
171	Conditional Random Fields as Recurrent Neural Networks	Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr	To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling.
172	The One Triangle Three Parallelograms Sampling Strategy and Its Application in Shape Regression	Mikael Nilsson	The purpose of this paper is threefold.
173	Boosting Object Proposals: From Pascal to COCO	Jordi Pont-Tuset, Luc Van Gool	In sight of these results, we propose various lines of research to take advantage of the new benchmark and improve the techniques.
174	Secrets of GrabCut and Kernel K-Means	Meng Tang, Ismail Ben Ayed, Dmitrii Marin, Yuri Boykov	We propose an alternative approach to color clustering using kernel K-means energy with well-known properties such as non-linear separation and scalability to higher-dimensional feature spaces.
175	Video Matting via Sparse and Low-Rank Representation	Dongqing Zou, Xiaowu Chen, Guangying Cao, Xiaogang Wang	We introduce a novel method of video matting via sparse and low-rank representation.
176	Joint Object and Part Segmentation Using Deep Learned Potentials	Peng Wang, Xiaohui Shen, Zhe Lin, Scott Cohen, Brian Price, Alan L. Yuille	In this paper, we propose a joint solution that tackles semantic object and part segmentation simultaneously, in which higher object-level context is provided to guide part segmentation, and more detailed part-level localization is utilized to refine object segmentation.
177	Low-Rank Tensor Constrained Multiview Subspace Clustering	Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao	In this paper, we explore the problem of multiview subspace clustering.
178	BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies	Jiangping Wang, Kai Ma, Vivek Kumar Singh, Thomas Huang, Terrence Chen	We address this problem by proposing a novel holistic human body shape descriptor called BodyPrint.
179	The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals	Ahmad Humayun, Fuxin Li, James M. Rehg	We propose a new energy minimization framework incorporating geodesic distances between segments which solves this problem.
180	Contour Guided Hierarchical Model for Shape Matching	Yuanqi Su, Yuehu Liu, Bonan Cuan, Nanning Zheng	In the paper, we present a novel algorithm that reconsiders these connections and reduces the global matching to a set of interrelated local matching.
181	Robust Image Segmentation Using Contour-Guided Color Palettes	Xiang Fu, Chien-Yi Wang, Chen Chen, Changhu Wang, C.-C. Jay Kuo	Robust Image Segmentation Using Contour-Guided Color Palettes
182	Joint Optimization of Segmentation and Color Clustering	Ekaterina Lobacheva, Olga Veksler, Yuri Boykov	We propose to make clustering an integral part of segmentation, by including a new clustering term in the energy function.
183	BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation	Jifeng Dai, Kaiming He, Jian Sun	In this paper, we propose a method that achieves competitive accuracy but only requires easily obtained bounding box annotations.
184	Detection and Segmentation of 2D Curved Reflection Symmetric Structures	Ching L. Teo, Cornelia Fermuller, Yiannis Aloimonos	In this work, we propose a complete approach that links the detection of curved reflection symmetries to produce symmetry-constrained segments of structures/regions in real images with clutter.
185	Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories	Mihai Marian Puscas, Enver Sangineto, Dubravko Culibrk, Nicu Sebe	Specifically, we propose to use Dense Trajectories in order to robustly match and track candidate boxes over different frames.
186	Compositional Hierarchical Representation of Shape Manifolds for Classification of Non-Manifold Shapes	Mete Ozay, Umit Rusen Aktas, Jeremy L. Wyatt, Ales Leonardis	To this end, we introduce a framework to implement the indexing mechanisms for the employment of the vocabulary for structural shape classification.
187	Shell PCA: Statistical Shape Modelling in Shell Space	Chao Zhang, Behrend Heeren, Martin Rumpf, William A. P. Smith	In this paper we describe how to perform Principal Components Analysis in “shell space”.
188	Learning to Combine Mid-Level Cues for Object Proposal Generation	Tom Lee, Sanja Fidler, Sven Dickinson	In this paper, we introduce Parametric Min-Loss (PML), a novel structured learning framework for parametric energy functions.
189	Enhancing Road Maps by Parsing Aerial Images Around the World	Gellert Mattyus, Shenlong Wang, Sanja Fidler, Raquel Urtasun	In this paper we propose to exploit aerial images in order to enhance freely available world maps.
190	Probabilistic Appearance Models for Segmentation and Classification	Julia Kruger, Jan Ehrhardt, Heinz Handels	We propose the use of probabilistic correspondences for statistical appearance models by incorporating appearance information into the framework.
191	A Randomized Ensemble Approach to Industrial CT Segmentation	Hyojin Kim, Jayaraman Jayaraman J. Thiagarajan, Peer-Timo Bremer	This paper presents a new ensemble-based segmentation framework for industrial CT images demonstrating that comparatively simple models and randomization strategies can significantly improve the result over existing techniques.
192	Semi-Supervised Normalized Cuts for Image Segmentation	Selene E. Chew, Nathan D. Cahill	In this paper, we reformulate NCuts to allow both sets of constraints to be handled in a soft manner, enabling the user to tune the degree to which the constraints are satisfied.
193	StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images	Ran Ju, Tongwei Ren, Gangshan Wu	In this paper, we propose a contour based method which searches for consistent object contours instead of regions.
194	Semantic Segmentation of RGBD Images With Mutex Constraints	Zhuo Deng, Sinisa Todorovic, Longin Jan Latecki	In this paper, we address the problem of semantic scene segmentation of RGB-D images of indoor scenes.
195	Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation	George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, Alan L. Yuille	We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings.
196	Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts	Margret Keuper, Evgeny Levinkov, Nicolas Bonneel, Guillaume Lavoue, Thomas Brox, Bjorn Andres	We propose a generalization of the MP with long-range terms (LMP).
197	Parsimonious Labeling	Puneet K. Dokania, M. Pawan Kumar	We propose a new family of discrete energy minimization problems, which we call parsimonious labeling.
198	Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions	Yuri Boykov, Hossam Isack, Carl Olsson, Ismail Ben Ayed	Our general ideas apply to continuous or discrete energy formulations in segmentation, stereo, and other reconstruction problems.
199	Entropy Minimization for Convex Relaxation Approaches	Mohamed Souiai, Martin R. Oswald, Youngwook Kee, Junmo Kim, Marc Pollefeys, Daniel Cremers	In this paper, we propose a novel relaxation technique which incorporates the entropy of the objective variable as a measure of relaxation tightness.
200	Adaptively Unified Semi-Supervised Dictionary Learning With Active Points	Xiaobo Wang, Xiaojie Guo, Stan Z. Li	In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously.
201	Constrained Convolutional Neural Networks for Weakly Supervised Segmentation	Deepak Pathak, Philipp Krahenbuhl, Trevor Darrell	We present an approach to learn a dense pixel-wise labeling from image-level tags.
202	A Multiscale Variable-Grouping Framework for MRF Energy Minimization	Omer Meir, Meirav Galun, Stav Yagev, Ronen Basri, Irad Yavneh	We present a multiscale approach for minimizing the energy associated with Markov Random Fields (MRFs) with energy functions that include arbitrary pairwise potentials.
203	Inferring M-Best Diverse Labelings in a Single One	Alexander Kirillov, Bogdan Savchynskyy, Dmitrij Schlesinger, Dmitry Vetrov, Carsten Rother	We show that the method of Batra et al. can be considered as a greedy approximate algorithm for our model, whereas we introduce an efficient specialized optimization technique for it, based on alpha-expansion.
204	Convolutional Sparse Coding for Image Super-Resolution	Shuhang Gu, Wangmeng Zuo, Qi Xie, Deyu Meng, Xiangchu Feng, Lei Zhang	In this paper, we propose a convolutional sparse coding (CSC) based SR (CSC-SR) method to address the consistency issue.
205	A Wavefront Marching Method for Solving the Eikonal Equation on Cartesian Grids	Brais Cancela, Marcos Ortega, Manuel G. Penedo	This paper presents a new wavefront propagation method for dealing with the classic Eikonal equation.
206	A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer	Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh	Motivated by these needs, this paper presents an optimization scheme to solve generalized eigenvalue problems (GEP) involving a (nonsmooth) regularizer.
207	Optimizing Expected Intersection-Over-Union With Candidate-Constrained CRFs	Faruk Ahmed, Dany Tarlow, Dhruv Batra	We study the question of how to make loss-aware predictions in image segmentation settings where the evaluation function is the Intersection-over-Union (IoU) measure that is used widely in evaluating image segmentation systems.
208	Higher-Order Inference for Multi-Class Log-Supermodular Models	Jian Zhang, Josip Djolonga, Andreas Krause	We formalize this task as performing inference in log-supermodular models under partition constraints, and present an efficient variational inference technique.
209	Depth-Based Hand Pose Estimation: Data, Methods, and Challenges	James S. Supancic III, Gregory Rogez, Yi Yang, Jamie Shotton, Deva Ramanan	To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes.
210	Adaptive Dither Voting for Robust Spatial Verification	Xiaomeng Wu, Kunio Kashino	To handle this problem, we propose a new method, called adaptive dither voting, for robust spatial verification.
211	Alternating Co-Quantization for Cross-Modal Hashing	Go Irie, Hiroyuki Arai, Yukinobu Taniguchi	We propose a method to minimize the binary quantization errors, which is tailored to cross-modal hashing.
212	Learning Deep Representation With Large-Scale Attributes	Wanli Ouyang, Hongyang Li, Xingyu Zeng, Xiaogang Wang	This paper contributes a large-scale object attribute database (The dataset is available on www.ee.cuhk.edu.hk/ xgwang/ImageNetAttribute.html) that contains rich attribute annotations (over 300 attributes) for ~180k samples and 494 object classes.
213	Deep Learning Strong Parts for Pedestrian Detection	Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang	Unlike previous deep models that directly learned a single detector for pedestrian detection, we propose DeepParts, which consists of extensive part detectors.
214	Flowing ConvNets for Human Pose Estimation in Videos	Tomas Pfister, James Charles, Andrew Zisserman	The objective of this work is human pose estimation in videos, where multiple frames are available.
215	Top Rank Supervised Binary Coding for Visual Search	Dongjin Song, Wei Liu, Rongrong Ji, David A. Meyer, John R. Smith	In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information.
216	BubbLeNet: Foveated Imaging for Visual Discovery	Kevin Matzen, Noah Snavely	We propose a new method for turning an Internet-scale corpus of categorized images into a small set of human-interpretable discriminative visual elements using powerful tools based on deep learning.
217	PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization Using Hash Tables	Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa	We propose the product quantization table (PQTable), a product quantization-based hash table that is fast and requires neither parameter tuning nor training steps.
218	Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions	Sven Bambach, Stefan Lee, David J. Crandall, Chen Yu	We develop methods to locate and distinguish between hands in egocentric video using strong appearance models with Convolutional Neural Networks, and introduce a simple candidate region generation approach that outperforms existing techniques at a fraction of the computational cost.
219	Fast and Accurate Head Pose Estimation via Random Projection Forests	Donghoon Lee, Ming-Hsuan Yang, Songhwai Oh	In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image.
220	An MRF-Poselets Model for Detecting Highly Articulated Humans	Duc Thanh Nguyen, Minh-Khoi Tran, Sai-Kit Yeung	This paper proposes a novel part-based model built upon poselets, a notion of parts, and Markov Random Field (MRF) for modelling the human body structure under the variation of human poses and viewpoints.
221	Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation	Lianrui Fu, Junge Zhang, Kaiqi Huang	We propose an occlusion aware graphical model which is able to model both self-occlusion and occlusion by the other objects simultaneously.
222	Relaxing From Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging	Jianlong Fu, Yue Wu, Tao Mei, Jinqiao Wang, Hanqing Lu, Yong Rui	In this paper, we propose a weakly-supervised deep learning model which can be trained from the readily available Web images to relax the dependence on human labors and scale up to arbitrary tags (categories).
223	Visual Phrases for Exemplar Face Detection	Vijay Kumar, Anoop Namboodiri, C. V. Jawahar	In this paper, we propose a novel approach that incorporates higher order information in the voting process.
224	Spatial Semantic Regularisation for Large Scale Object Detection	Damian Mrowca, Marcus Rohrbach, Judy Hoffman, Ronghang Hu, Kate Saenko, Trevor Darrell	We propose a new multi-class spatial semantic regularisation method based on affinity propagation clustering, which simultaneously optimises across all categories and all proposed locations in the image, to improve both the localisation and categorisation of selected detection proposals.
225	Human Pose Estimation in Videos	Dong Zhang, Mubarak Shah	In this paper, we present a method to estimate a sequence of human poses in unconstrained videos.
226	Contour Box: Rejecting Object Proposals Without Explicit Closed Contours	Cewu Lu, Shu Liu, Jiaya Jia, Chi-Keung Tang	We propose a new measure subject to the completeness and tightness constraints, where the optimized closed contour should be tightly bounded within an object proposal.
227	Registering Images to Untextured Geometry Using Average Shading Gradients	Tobias Plotz, Stefan Roth	In this paper we consider the registration of photographs to 3D models even when no texture information is available.
228	Robust Nonrigid Registration by Convex Optimization	Qifeng Chen, Vladlen Koltun	We present an approach to nonrigid registration of 3D surfaces.
229	Robust and Optimal Sum-of-Squares-Based Point-to-Plane Registration of Image Sets and Structured Scenes	Danda Pani Paudel, Adlane Habed, Cedric Demonceaux, Pascal Vasseur	For the first time in this paper, a Sum-of-Squares optimization theory framework is employed for identifying point-to-plane mismatches (i.e. outliers) with certainty.
230	MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation	Chi Zhang, Zhiwei Li, Yanhua Cheng, Rui Cai, Hongyang Chao, Yong Rui	We present a novel global stereo model designed for view interpolation.
231	CV-HAZOP: Introducing Test Data Validation for Computer Vision	Oliver Zendel, Markus Murschitz, Martin Humenberger, Wolfgang Herzner	In this paper we propose a new solution answering these questions using a standard procedure devised by the safety community to validate complex systems: The Hazard and Operability Analysis (HAZOP).
232	Structure From Motion Using Structure-Less Resection	Enliang Zheng, Changchang Wu	In this work, we take the collection of already reconstructed cameras as a generalized camera, and determine the absolute pose of a candidate pinhole camera from pure 2D correspondences, which we call it semi-generalized camera pose problem.
233	Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-View Stereo	Runze Zhang, Shiwei Li, Tian Fang, Siyu Zhu, Long Quan	In this paper, we propose an optimal decomposition approach to large-scale multi-view stereo from an initial sparse reconstruction.
234	Higher-Order CRF Structural Segmentation of 3D Reconstructed Surfaces	Jingbo Liu, Jinglu Wang, Tian Fang, Chiew-Lan Tai, Long Quan	In this paper, we propose a structural segmentation algorithm to partition multi-view stereo reconstructed surfaces of large-scale urban environments into structural segments.
235	Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition	Torsten Sattler, Michal Havlena, Filip Radenovic, Konrad Schindler, Marc Pollefeys	Here we explore an orthogonal strategy, which uses all the 3D points and standard sampling, but performs feature matching implicitly, by quantization into a fine vocabulary.
236	Globally Optimal 2D-3D Registration From Points or Lines Without Correspondences	Mark Brown, David Windridge, Jean-Yves Guillemaut	We present a novel approach to 2D-3D registration from points or lines without correspondences.
237	The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms	Katrin Honauer, Lena Maier-Hein, Daniel Kondermann	We show that the RMS is of limited expressiveness for algorithm selection and introduce the HCI Stereo Metrics.
238	Merging the Unmatchable: Stitching Visually Disconnected SfM Models	Andrea Cohen, Torsten Sattler, Marc Pollefeys	In this paper, we present a combinatorial approach for solving this variant by automatically stitching multiple sides of a building together.
239	3D Fragment Reassembly Using Integrated Template Guidance and Fracture-Region Matching	Kang Zhang, Wuyi Yu, Mary Manhein, Warren Waggenspack, Xin Li	We develop a new algorithm to effectively integrate both guidance from a template and from matching of adjacent pieces’ fracture-regions.
240	Procedural Editing of 3D Building Point Clouds	Ilke Demir, Daniel G. Aliaga, Bedrich Benes	In our work, we tackle the problem of point cloud completion and editing and we approach it via inverse procedural modeling.
241	Semantically-Aware Aerial Reconstruction From Multi-Modal Data	Randi Cabezas, Julian Straub, John W. Fisher III	We propose a probabilistic generative model for inferring semantically-informed aerial reconstructions from multi-modal data within a consistent mathematical framework. We introduce a new multi-modal synthetic dataset in order to provide quantitative performance analysis.
242	Guaranteed Outlier Removal for Rotation Search	Alvaro Parra Bustos, Tat-Jun Chin	In this paper, we propose a novel outlier removal technique for rotation search.
243	Peeking Template Matching for Depth Extension	Simon Korman, Eyal Ofek, Shai Avidan	We propose a method that extends a given depth image into regions in 3D that are not visible from the point of view of the camera.
244	Deformable 3D Fusion: From Partial Dynamic 3D Observations to Complete 4D Models	Weipeng Xu, Mathieu Salzmann, Yongtian Wang, Yue Liu	In this paper, we introduce a template-less 4D reconstruction method that incrementally fuses highly-incomplete 3D observations of a deforming object, and generates a complete, temporally-coherent shape representation of the object.
245	Non-Parametric Structure-Based Calibration of Radially Symmetric Cameras	Federico Camposeco, Torsten Sattler, Marc Pollefeys	We propose a novel two-step method for estimating the intrinsic and extrinsic calibration of any radially symmetric camera, including non-central systems.
246	Exploiting Object Similarity in 3D Reconstruction	Chen Zhou, Fatma Guney, Yizhou Wang, Andreas Geiger	In this paper, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene.
247	You Are Here: Mimicking the Human Thinking Process in Reading Floor-Plans	Hang Chu, Dong Ki Kim, Tsuhan Chen	More precisely, we introduce a new and useful task of locating an user in the floor-plan, by using only a camera and a floor-plan without any other prior information.
248	MAP Disparity Estimation Using Hidden Markov Trees	Eric T. Psota, Jedrzej Kowalczuk, Mateusz Mittek, Lance C. Perez	A new method is introduced for stereo matching that operates on minimum spanning trees (MSTs) generated from the images.
249	Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints	Meirav Galun, Tal Amir, Tal Hassner, Ronen Basri, Yaron Lipman	We introduce a novel method that integrates a deformation model.
250	Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction	Xinxin Zuo, Chao Du, Sen Wang, Jiangbin Zheng, Ruigang Yang	In this paper we present a method of using standard multi-view images for 3D surface reconstruction of non-Lambertian objects.
251	Hierarchical Higher-Order Regression Forest Fields: An Application to 3D Indoor Scene Labelling	Trung T. Pham, Ian Reid, Yasir Latif, Stephen Gould	In this work we propose models with higher-order potentials to describe complex relational information from the 3D scenes.
252	Classical Scaling Revisited	Gil Shamai, Yonathan Aflalo, Michael Zibulevsky, Ron Kimmel	We present an efficient solver for Classical Scaling (a specific MDS model) by extending the distances measured from a subset of the points to the rest, while exploiting the smoothness property of the distance functions.
253	Dense Continuous-Time Tracking and Mapping With Rolling Shutter RGB-D Cameras	Christian Kerl, Jorg Stuckler, Daniel Cremers	We propose a dense continuous-time tracking and mapping method for RGB-D cameras.
254	Dense Image Registration and Deformable Surface Reconstruction in Presence of Occlusions and Minimal Texture	Dat Tien Ngo, Sanghyuk Park, Anne Jorstad, Alberto Crivellaro, Chang D. Yoo, Pascal Fua	In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score.
255	The Likelihood-Ratio Test and Efficient Robust Estimation	Andrea Cohen, Christopher Zach	We propose a new approach for jointly optimizing over model parameters and the inlier noise level based on the likelihood ratio test.
256	Reflection Modeling for Passive Stereo	Rahul Nair, Andrew Fitzgibbon, Daniel Kondermann, Carsten Rother	These two properties make inference of the model appear prohibitive, but we present evidence that inference is actually possible using a variant of patch match stereo.
257	Detailed Full-Body Reconstructions of Moving People From Monocular RGB-D Sequences	Federica Bogo, Michael J. Black, Matthew Loper, Javier Romero	We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product.
258	Efficient Solution to the Epipolar Geometry for Radially Distorted Cameras	Zuzana Kukelova, Jan Heller, Martin Bujnak, Andrew Fitzgibbon, Tomas Pajdla	In this paper, we present a new efficient solution to this problem that uses 10 image correspondences.
259	Learning a Descriptor-Specific 3D Keypoint Detector	Samuele Salti, Federico Tombari, Riccardo Spezialetti, Luigi Di Stefano	To overcome these shortcomings, we cast 3D keypoint detection as a binary classification between points whose support can be correctly matched by a predefined 3D descriptor or not, thereby learning a descriptor-specific detector that adapts seamlessly to different scenarios.
260	Component-Wise Modeling of Articulated Objects	Valsamis Ntouskos, Marta Sanzari, Bruno Cafaro, Federico Nardi, Fabrizio Natola, Fiora Pirri, Manuel Ruiz	We introduce a novel framework for modeling articulated objects based on the aspects of their components.
261	A Collaborative Filtering Approach to Real-Time Hand Pose Estimation	Chiho Choi, Ayan Sinha, Joon Hee Choi, Sujin Jang, Karthik Ramani	Inspired by fast and accurate matrix factorization techniques for collaborative filtering, we develop a real-time algorithm for estimating the hand pose from RGB-D data of a commercial depth camera.
262	On the Equivalence of Moving Entrance Pupil and Radial Distortion for Camera Calibration	Avinash Kumar, Narendra Ahuja	In this paper, we show using a thick-lens imaging model, that the variation of entrance pupil location as a function of incident image ray angle is directly responsible for radial distortion in captured images.
263	A Linear Generalized Camera Calibration From Three Intersecting Reference Planes	Mai Nishimura, Shohei Nobuhara, Takashi Matsuyama, Shinya Shimizu, Kensaku Fujii	This paper presents a new generalized (or ray-pixel, raxel) camera calibration algorithm for camera systems involving distortions by unknown refraction and reflection processes.
264	Towards Pointless Structure From Motion: 3D Reconstruction and Camera Parameters From General 3D Curves	Irina Nurutdinova, Andrew Fitzgibbon	We show how 3D curves can be used to refine camera position estimation in challenging low-texture scenes.
265	Attributed Grammars for Joint Estimation of Human Attributes, Part and Pose	Seyoung Park, Song-Chun Zhu	In this paper, we are interested in developing compositional models to explicit representing pose, parts and attributes and tackling the tasks of attribute recognition, pose estimation and part localization jointly.
266	Real-Time Pose Estimation Piggybacked on Object Detection	Roman Juranek, Adam Herout, Marketa Dubska, Pavel Zemcik	We present an object detector coupled with pose estimation directly in a single compact and simple model, where the detector shares extracted image features with the pose estimator.
267	Understanding and Predicting Image Memorability at a Large Scale	Aditya Khosla, Akhil S. Raju, Antonio Torralba, Aude Oliva	Here, we introduce a novel experimental procedure to objectively measure human memory, building the largest annotated image memorability dataset to date (with 60,000 labeled images from a diverse array of sources).
268	Multiple Granularity Descriptors for Fine-Grained Categorization	Dequan Wang, Zhiqiang Shen, Jie Shao, Wei Zhang, Xiangyang Xue, Zheng Zhang	We leverage the fact that a subordinate-level object already has other labels in its ontology tree.
269	Guiding the Long-Short Term Memory Model for Image Caption Generation	Xu Jia, Efstratios Gavves, Basura Fernando, Tinne Tuytelaars	In this work we focus on the problem of image caption generation.
270	Just Noticeable Differences in Visual Attributes	Aron Yu, Kristen Grauman	We explore the problem of predicting “just noticeable differences” in a visual attribute.
271	VQA: Visual Question Answering	Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh	We propose the task of free-form and open-ended Visual Question Answering (VQA). We provide a dataset containing 0.25M images, 0.76M questions, and 10M answers (www.visualqa.org), and discuss the information it provides.
272	Localize Me Anywhere, Anytime: A Multi-Task Point-Retrieval Approach	Guoyu Lu, Yan Yan, Li Ren, Jingkuan Song, Nicu Sebe, Chandra Kambhamettu	The main contribution of our paper is that we use a 3D model reconstructed by a short video as the query to realize 3D-to-3D localization under a multi-task point retrieval framework.
273	Dense Optical Flow Prediction From a Static Image	Jacob Walker, Abhinav Gupta, Martial Hebert	In this work, we present a convolutional neural network (CNN) based approach for motion prediction.
274	Unsupervised Domain Adaptation for Zero-Shot Learning	Elyor Kodirov, Tao Xiang, Zhenyong Fu, Shaogang Gong	In this paper a novel ZSL method is proposed based on unsupervised domain adaptation.
275	Visual Madlibs: Fill in the Blank Description Generation and Question Answering	Licheng Yu, Eunbyung Park, Alexander C. Berg, Tamara L. Berg	In this paper, we introduce a new dataset consisting of 360,001 focused natural language descriptions for 10,738 images.
276	Actions and Attributes From Wholes and Parts	Georgia Gkioxari, Ross Girshick, Jitendra Malik	We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision.
277	DeepBox: Learning Objectness With Convolutional Networks	Weicheng Kuo, Bharath Hariharan, Jitendra Malik	We argue for a data-driven, semantic approach for ranking object proposals.
278	Active Object Localization With Deep Reinforcement Learning	Juan C. Caicedo, Svetlana Lazebnik	We present an active detection model for localizing objects in scenes.
279	Scene-Domain Active Part Models for Object Representation	Zhou Ren, Chaohui Wang, Alan L. Yuille	In this paper, we are interested in enhancing the expressivity and robustness of part-based models for object representation, in the common scenario where the training data are based on 2D images.
280	A Unified Multiplicative Framework for Attribute Learning	Kongming Liang, Hong Chang, Shiguang Shan, Xilin Chen	In this paper, we propose a unified multiplicative framework for attribute learning, which tackles the key problems.
281	Contractive Rectifier Networks for Nonlinear Maximum Margin Classification	Senjian An, Munawar Hayat, Salman H. Khan, Mohammed Bennamoun, Farid Boussaid, Ferdous Sohel	To find the optimal nonlinear separating boundary with maximum margin in the input data space, this paper proposes Contractive Rectifier Networks (CRNs), wherein the hidden-layer transformations are restricted to be contraction mappings.
282	Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization	Zhe Xu, Shaoli Huang, Ya Zhang, Dacheng Tao	We propose a new method for fine-grained object recognition that employs part-level annotations and deep convolutional neural networks (CNNs) in a unified framework.
283	Learning Like a Child: Fast Novel Visual Concept Learning From Sentence Descriptions of Images	Junhua Mao, Xu Wei, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille	In this paper, we address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions.
284	Learning Common Sense Through Visual Abstraction	Ramakrishna Vedantam, Xiao Lin, Tanmay Batra, C. Lawrence Zitnick, Devi Parikh	Our key insight is that while visual common sense is depicted in visual content, it is the semantic features that are relevant and not low-level pixel information.
285	Domain Generalization for Object Recognition With Multi-Task Autoencoders	Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, David Balduzzi	We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition.
286	Square Localization for Efficient and Accurate Object Detection	Cewu Lu, Yongyi Lu, Hao Chen, Chi-Keung Tang	The key contribution of this paper is the compact square object localization, which relaxes the exhaustive sliding window from testing all windows of different combinations of aspect ratios.
287	Box Aggregation for Proposal Decimation: Last Mile of Object Detection	Shu Liu, Cewu Lu, Jiaya Jia	We explain why it works using some statistics in this paper.
288	DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers	Amir Ghodrati, Ali Diba, Marco Pedersoli, Tinne Tuytelaars, Luc Van Gool	In this paper we evaluate the quality of the activation layers of a convolutional neural network (CNN) for the generation of object proposals.
289	Semantic Segmentation With Object Clique Potential	Xiaojuan Qi, Jianping Shi, Shu Liu, Renjie Liao, Jiaya Jia	In this paper, we propose an object clique potential for semantic segmentation.
290	Automatic Concept Discovery From Parallel Text and Visual Corpora	Chen Sun, Chuang Gan, Ram Nevatia	We propose an automatic visual concept discovery algorithm using parallel text and visual corpora; it filters text terms based on the visual discriminative power of the associated images, and groups them into concepts using visual and semantic similarities.
291	Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning	Ragav Venkatesan, Parag Chandakkar, Baoxin Li	In this paper, we analyze the MIL feature space using modified versions of traditional non-parametric techniques like the Parzen window and k-nearest-neighbour, and develop a learning approach employing distances to k-nearest neighbours of a point in the feature space.
292	Monocular Object Instance Segmentation and Depth Ordering With CNNs	Ziyu Zhang, Alexander G. Schwing, Sanja Fidler, Raquel Urtasun	In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image.
293	Multimodal Convolutional Neural Networks for Matching Image and Sentence	Lin Ma, Zhengdong Lu, Lifeng Shang, Hang Li	In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence.
294	Structural Kernel Learning for Large Scale Multiclass Object Co-Detection	Zeeshan Hayder, Xuming He, Mathieu Salzmann	Here, we address the problem of multiclass object co-detection for large scale datasets.
295	Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models	Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, Svetlana Lazebnik	This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains linking mentions of the same entities in images, as well as 276k manually annotated bounding boxes corresponding to each entity.
296	Predicting Depth, Surface Normals and Semantic Labels With a Common Multi-Scale Convolutional Architecture	David Eigen, Rob Fergus	In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling.
297	AttentionNet: Aggregating Weak Directions for Accurate Object Detection	Donggeun Yoo, Sunggyun Park, Joon-Young Lee, Anthony S. Paek, In So Kweon	We present a novel detection method using a deep convolutional neural network (CNN), named AttentionNet.
298	Common Subspace for Model and Similarity: Phrase Learning for Caption Generation From Images	Yoshitaka Ushiku, Masataka Yamaguchi, Yusuke Mukuta, Tatsuya Harada	In this paper, we propose a novel phrase-learning method: Common Subspace for Model and Similarity (CoSMoS).
299	3D-Assisted Feature Synthesis for Novel Views of an Object	Hao Su, Fan Wang, Eric Yi, Leonidas J. Guibas	In this paper, given a single input image of an object, we synthesize its features for other views, leveraging an existing modestly-sized 3D model collection of related but not identical objects.To accomplish this, we study the relationship of image patches between different views of the same object, seeking what we call surrogate patches — patches in one view whose feature content predicts well the features of a patch in another view. Based upon these surrogate relationships, we can create feature sets for all views of the latent object on a per patch basis, providing us an augmented multi-view representation of the object.
300	Render for CNN: Viewpoint Estimation in Images Using CNNs Trained With Rendered 3D Model Views	Hao Su, Charles R. Qi, Yangyan Li, Leonidas J. Guibas	Inspired by the growing availability of 3D models, we propose a framework to address both issues by combining render-based image synthesis and CNNs (Convolutional Neural Networks).
301	Lost Shopping! Monocular Localization in Large Indoor Spaces	Shenlong Wang, Sanja Fidler, Raquel Urtasun	In this paper we propose a novel approach to localization in very large indoor spaces (i.e., 200+ store shopping malls) that takes a single image and a floor plan of the environment as input.
302	Camera Pose Voting for Large-Scale Image-Based Localization	Bernhard Zeisl, Torsten Sattler, Marc Pollefeys	In this work we study the benefits and limitations of spatial verification compared to appearance-based filtering.
303	MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking	Thibaut Durand, Nicolas Thome, Matthieu Cord	In this work, we propose a novel Weakly Supervised Learning (WSL) framework dedicated to learn discriminative part detectors from images annotated with a global label.
304	DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving	Chenyi Chen, Ari Seff, Alain Kornhauser, Jianxiong Xiao	In this paper, we propose a third paradigm: a direct perception approach to estimate the affordance for driving.
305	Active Transfer Learning With Zero-Shot Priors: Reusing Past Datasets for Future Tasks	Efstratios Gavves, Thomas Mensink, Tatiana Tommasi, Cees G. M. Snoek, Tinne Tuytelaars	On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort.
306	HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition	Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, Yizhou Yu	In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy.
307	Learning The Structure of Deep Convolutional Networks	Jiashi Feng, Trevor Darrell	In this work, we develop a novel method for automatically learning aspects of the structure of a deep model, in order to improve its performance, especially when labeled training data are scarce.
308	FlowNet: Learning Optical Flow With Convolutional Networks	Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox	In this paper we construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a large synthetic Flying Chairs dataset.
309	Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning	Chun-Guang Li, Zhouchen Lin, Honggang Zhang, Jun Guo	In this paper, we formulate the two stages of SSL into a unified optimization framework, which learns both the affinity matrix and the unknown labels simultaneously.
310	Context-Guided Diffusion for Label Propagation on Graphs	Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt	Inspired by the success of diffusivity tensors for anisotropic diffusion in image processing, we presents anisotropic diffusion on graphs and the corresponding label propagation algorithm.
311	Learning to Rank Based on Subsequences	Basura Fernando, Efstratios Gavves, Damien Muselet, Tinne Tuytelaars	In this work we propose MidRank, which learns from moderately sized sub-sequences instead.
312	Unsupervised Learning of Visual Representations Using Videos	Xiaolong Wang, Abhinav Gupta	In this paper, we present a simple yet surprisingly powerful approach for unsupervised learning of CNN.
313	A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis	Sotirios P. Chatzis, Dimitrios Kosmopoulos	To resolve these issues, in this paper we introduce a convolutional nonparametric Bayesian sparse ICA architecture for overcomplete feature learning from high-dimensional data.
314	Robust Principal Component Analysis on Graphs	Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst	In this article, we introduce a new model called ‘Robust PCA on Graphs’ which incorporates spectral graph regularization into the Robust PCA framework.
315	Projection Bank: From High-Dimensional Data to Medium-Length Binary Codes	Li Liu, Mengyang Yu, Ling Shao	To target a better balance between computational efficiency and accuracies, in this paper, we propose a novel embedding method called Binary Projection Bank (BPB), which can effectively reduce the very high-dimensional representations to medium-dimensional binary codes without sacrificing accuracies.
316	Robust Optimization for Deep Regression	Vasileios Belagiannis, Christian Rupprecht, Gustavo Carneiro, Nassir Navab	In this work, we propose a regression model with ConvNets that achieves robustness to such outliers by minimizing Tukey’s biweight function, an M-estimator robust to outliers, as the loss function for the ConvNet.
317	Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition	Chengjiang Long, Gang Hua	In this paper, we propose a novel Gaussian process classifier model with multiple annotators for multi-class visual recognition.
318	Maximum-Margin Structured Learning With Deep Networks for 3D Human Pose Estimation	Sijin Li, Weichen Zhang, Antoni B. Chan	We test our framework on the Human3.6m dataset and obtain state-of-the-art results compared to other recent methods.
319	An Exploration of Parameter Redundancy in Deep Networks With Circulant Projections	Yu Cheng, Felix X. Yu, Rogerio S. Feris, Sanjiv Kumar, Alok Choudhary, Shi-Fu Chang	We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection.
320	Additive Nearest Neighbor Feature Maps	Zhenzhen Wang, Xiao-Tong Yuan, Qingshan Liu, Shuicheng Yan	In this paper, we present a concise framework to approximately construct feature maps for nonlinear additive kernels such as the Intersection, Hellinger’s, and Chi^2 kernels.
321	Understanding Deep Features With Computer-Generated Imagery	Mathieu Aubry, Bryan C. Russell	We introduce an approach for analyzing the variation of features generated by convolutional neural networks (CNNs) trained on large image datasets with respect to scene factors that occur in natural images.
322	Interpolation on the Manifold of K Component GMMs	Hyunwoo J. Kim, Nagesh Adluru, Monami Banerjee, Baba C. Vemuri, Vikas Singh	In this paper, we study the Gaussian mixture model (GMM) representation of the PDFs motivated by its numerous attractive features.
323	Context-Aware CNNs for Person Head Detection	Tuan-Hung Vu, Anton Osokin, Ivan Laptev	In this work we focus on detecting human heads in natural scenes. To train and test our model, we introduce a large dataset with 369,846 human heads annotated in 224,740 movie frames.
324	Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting	Hanzi Wang, Guobao Xiao, Yan Yan, David Suter	In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers.
325	Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple	Oren Freifeld, Soren Hauberg, Kayhan Batmanghelich, John W. Fisher III	We propose novel finite-dimensional spaces of R – R transformations, n [?]
326	Entropy-Based Latent Structured Output Prediction	Diane Bouchacourt, Sebastian Nowozin, M. Pawan Kumar	In order to aid their application in computer vision, we study these generalizations with the aim of identifying their strengths and weaknesses.
327	Fast Orthogonal Projection Based on Kronecker Product	Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, Shi-Fu Chang	We propose a family of structured matrices to speed up orthogonal projections for high-dimensional data commonly seen in computer vision applications.
328	PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization	Alex Kendall, Matthew Grimes, Roberto Cipolla	We present a robust and real-time monocular six degree of freedom relocalization system.
329	Predicting Multiple Structured Visual Interpretations	Debadeepta Dey, Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell	We present a simple approach for producing a small number of structured visual outputs which have high recall, for a variety of tasks including monocular pose estimation and semantic scene segmentation.
330	Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks	Chunshui Cao, Xianming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, Thomas S. Huang	In this paper, we will briefly introduce the background of feedbacks in the human visual cortex, which motivates us to develop a computational feedback mechanism in the deep neural networks.
331	Matrix Backpropagation for Deep Networks With Structured Layers	Catalin Ionescu, Orestis Vantzos, Cristian Sminchisescu	In this paper we propose a sound mathematical apparatus to formally integrate global structured computation into deep computation architectures.
332	Introducing Geometry in Active Learning for Image Segmentation	Ksenia Konyushkova, Raphael Sznitman, Pascal Fua	We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes.
333	Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition	Heechul Jung, Sihaeng Lee, Junho Yim, Sunjeong Park, Junmo Kim	In this paper, to reduce this effort, a deep learning technique, which is regarded as a tool to automatically extract useful features from raw data, is adopted.
334	Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression	Takuya Narihira, Michael Maire, Stella X. Yu	We introduce a new approach to intrinsic image decomposition, the task of decomposing a single image into albedo and shading components.
335	Face Flow	Patrick Snape, Anastasios Roussos, Yannis Panagakis, Stefanos Zafeiriou	In this paper, we propose a method for the robust and efficient computation of multi-frame optical flow in an expressive sequence of facial images.
336	Discriminative Low-Rank Tracking	Yao Sui, Yafei Tang, Li Zhang	In this work, we exploit the advantages of the both approaches to achieve a robust tracker.
337	SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking	Han-Ul Kim, Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim	A simple yet effective object descriptor for visual tracking is proposed in this paper.
338	Live Repetition Counting	Ofir Levy, Lior Wolf	Live Repetition Counting
339	Near-Online Multi-Target Tracking With Aggregated Local Flow Descriptor	Wongun Choi	In this paper, we tackle two key aspects of multiple target tracking problem: 1) designing an accurate affinity measure to associate detections and 2) implementing an efficient and accurate (near) online multiple target tracking algorithm.
340	Multi-Kernel Correlation Filter for Visual Tracking	Ming Tang, Jiayi Feng	In this paper, we will derive a multi-kernel correlation filter (MKCF) based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features to further improve the performance.
341	Joint Probabilistic Data Association Revisited	Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid	In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.
342	Tracking-by-Segmentation With Online Gradient Boosting Decision Tree	Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han	We propose an online tracking algorithm that adaptively models target appearances based on an online gradient boosting decision tree.
343	Exploring Causal Relationships in Visual Object Tracking	Karel Lebeda, Simon Hadfield, Richard Bowden	In this paper we explore these relationships, and pro- vide statistical tools to detect and quantify them; these are based on transfer entropy and stem from information the- ory.
344	Hierarchical Convolutional Features for Visual Tracking	Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang	In this paper, we exploit features extracted from deep convolutional neural networks trained on object recognition datasets to improve tracking accuracy and robustness.
345	Robust Non-Rigid Motion Tracking and Surface Reconstruction Using L0 Regularization	Kaiwen Guo, Feng Xu, Yangang Wang, Yebin Liu, Qionghai Dai	We present a new motion tracking method to robustly reconstruct non-rigid geometries and motions from single view depth inputs captured by a consumer depth sensor.
346	Online Object Tracking With Proposal Selection	Yang Hua, Karteek Alahari, Cordelia Schmid	In this paper, we address this problem by formulating it as a proposal selection task and making two contributions.
347	Understanding and Diagnosing Visual Tracking Systems	Naiyan Wang, Jianping Shi, Dit-Yan Yeung, Jiaya Jia	To address this issue, we propose a framework by breaking a tracker down into five constituent parts, namely, motion model, feature extractor, observation model, model updater, and ensemble post-processor.
348	Integrating Dashcam Views Through Inter-Video Mapping	Hsin-I Chen, Yi-Ling Chen, Wei-Tse Lee, Fan Wang, Bing-Yu Chen	In this paper, an inter-video mapping approach is proposed to integrate video footages from two dashcams installed on a preceding and its following vehicle to provide the illusion that the driver of the following vehicle can see-through the preceding one.
349	Visual Tracking With Fully Convolutional Networks	Lijun Wang, Wanli Ouyang, Xiaogang Wang, Huchuan Lu	We propose a new approach for general object tracking with fully convolutional neural network.
350	Multiple Feature Fusion via Weighted Entropy for Visual Tracking	Lin Ma, Jiwen Lu, Jianjiang Feng, Jie Zhou	In this paper, we propose a new data-adaptive visual tracking approach by using multiple feature fusion via weighted entropy.
351	Pedestrian Travel Time Estimation in Crowded Scenes	Shuai Yi, Hongsheng Li, Xiaogang Wang	In this paper, we target on the problem of estimating the statistic of pedestrian travel time within a period from an entrance to a destination in a crowded scene.
352	Unsupervised Synchrony Discovery in Human Interaction	Wen-Sheng Chu, Jiabei Zeng, Fernando De la Torre, Jeffrey F. Cohn, Daniel S. Messinger	We present an unsupervised approach to discover interpersonal synchrony, referred as to two or more persons preforming common actions in overlapping video frames or segments.
353	Efficient Video Segmentation Using Parametric Graph Partitioning	Chen-Ping Yu, Hieu Le, Gregory Zelinsky, Dimitris Samaras	In this work, we propose an efficient and robust video segmentation framework based on parametric graph partitioning (PGP), a fast, almost parameter free graph partitioning method that identifies and removes between-cluster edges to form node clusters.
354	Learning to Track for Spatio-Temporal Action Localization	Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid	We propose an effective approach for spatio-temporal action localization in realistic videos.
355	Unsupervised Object Discovery and Tracking in Video Collections	Suha Kwak, Minsu Cho, Ivan Laptev, Jean Ponce, Cordelia Schmid	We formulate the problem as a combination of two complementary processes: discovery and tracking.
356	Car That Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models	Ashesh Jain, Hema S. Koppula, Bharad Raghavan, Shane Soh, Ashutosh Saxena	We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers.
357	Activity Auto-Completion: Predicting Human Activities From Partial Videos	Zhen Xu, Laiyun Qing, Jun Miao	In this paper, we propose an activity auto-completion (AAC) model for human activity prediction by formulating activity prediction as a query auto-completion (QAC) problem in information retrieval.
358	Person Re-Identification With Correspondence Structure Learning	Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang	This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.
359	Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps	Kang Dang, Jiong Yang, Junsong Yuan	We propose an efficient online video filtering method, called adaptive exponential filtering (AES) to refine pixel prediction maps.
360	P-CNN: Pose-Based CNN Features for Action Recognition	Guilhem Cheron, Ivan Laptev, Cordelia Schmid	To this end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN) for action recognition.
361	Fully Connected Object Proposals for Video Segmentation	Federico Perazzi, Oliver Wang, Markus Gross, Alexander Sorkine-Hornung	We present a novel approach to video segmentation using multiple object proposals.
362	Video Segmentation With Just a Few Strokes	Naveen Shankar Nagaraja, Frank R. Schmidt, Thomas Brox	We compare our approach to a diverse set of algorithms in terms of user effort and in terms of performance on common video segmentation benchmarks.
363	Actionness-Assisted Recognition of Actions	Ye Luo, Loong-Fah Cheong, An Tran	Actionness-Assisted Recognition of Actions
364	COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation	Viet-Quoc Pham, Tatsuo Kozakaya, Osamu Yamaguchi, Ryuzo Okada	This paper presents a patch-based approach for crowd density estimation in public scenes.
365	Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation	Saehoon Yi, Vladimir Pavlovic	We propose a Markov Random Field model for unconstrained video segmentation that relies on tight integration of multiple cues: vertices are defined from contour based superpixels, unary potentials from temporally smooth label likelihood and pairwise potentials from global structure of a video.
366	Motion Trajectory Segmentation via Minimum Cost Multicuts	Margret Keuper, Bjoern Andres, Thomas Brox	In this paper, we formulate the segmentation of a video sequence based on point trajectories as a minimum cost multicut problem.
367	Action Localization in Videos Through Context Walk	Khurram Soomro, Haroon Idrees, Mubarak Shah	This paper presents an efficient approach for localizing actions by learning contextual relations, in the form of relative locations between different video regions.
368	RGB-W: When Vision Meets Wireless	Alexandre Alahi, Albert Haque, Li Fei-Fei	Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional “quasi-free” modality, namely, the wireless signal (e.g., wifi or Bluetooth) emitted by individuals’ cell phones, referred to as RGB-W.
369	Action Detection by Implicit Intentional Motion Clustering	Wei Chen, Jason J. Corso	We conduct a quantitative analysis of intentional movement, and our findings motivate a new approach for implicit intentional movement extraction that is based on spatiotemporal trajectory clustering by leveraging the properties of intentional movement.
370	Simultaneous Foreground Detection and Classification With Hybrid Features	Jaemyun Kim, Adin Ramirez Rivera, Byungyong Ryu, Oksam Chae	In this paper, we propose a hybrid background model that relies on edge and non-edge features of the image to produce the model.
371	Training a Feedback Loop for Hand Pose Estimation	Markus Oberweger, Paul Wohlhart, Vincent Lepetit	We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image.
372	Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose	Danhang Tang, Jonathan Taylor, Pushmeet Kohli, Cem Keskin, Tae-Kyun Kim, Jamie Shotton	In this paper, we show that we can significantly improving upon black box optimization by exploiting high-level knowledge of the structure of the parameters and using a local surrogate energy function.
373	Panoptic Studio: A Massively Multiview System for Social Motion Capture	Hanbyul Joo, Hao Liu, Lei Tan, Lin Gui, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, Yaser Sheikh	We present an approach to capture the 3D structure and motion of a group of people engaged in a social interaction.
374	Where to Buy It: Matching Street Clothing Photos in Online Shops	M. Hadi Kiapour, Xufeng Han, Svetlana Lazebnik, Alexander C. Berg, Tamara L. Berg	In this paper, we define a new task, Exact Street to Shop, where our goal is to match a real-world example of a garment item to the same item in an online shop. We collect a new dataset for this application containing 404,683 shop photos collected from 25 different online retailers and 20,357 street photos, providing a total of 39,479 clothing item matches between street and shop photos.
375	Multi-Task Recurrent Neural Network for Immediacy Prediction	Xiao Chu, Wanli Ouyang, Wei Yang, Xiaogang Wang	In this paper, we propose to predict immediacy for interacting persons from still images. We propose a rich set of immediacy representations that help to predict immediacy from imperfect 1-person and 2-person pose estimation results.
376	Learning Complexity-Aware Cascades for Deep Pedestrian Detection	Zhaowei Cai, Mohammad Saberian, Nuno Vasconcelos	Learning Complexity-Aware Cascades for Deep Pedestrian Detection
377	Polarized 3D: High-Quality Depth Sensing With Polarization Cues	Achuta Kadambi, Vage Taamazyan, Boxin Shi, Ramesh Raskar	We propose a framework to combine surface normals from polarization (hereafter polarization normals) with an aligned depth map.
378	Airborne Three-Dimensional Cloud Tomography	Aviad Levis, Yoav Y. Schechner, Amit Aides, Anthony B. Davis	For light-matter interaction that accounts for multiple-scattering, we use the 3D radiative transfer equation as a forward model.
379	Leave-One-Out Kernel Optimization for Shadow Detection	Tomas F. Yago Vicente, Minh Hoai, Dimitris Samaras	The objective of this work is to detect shadows in images.
380	Removing Rain From a Single Image via Discriminative Sparse Coding	Yu Luo, Yong Xu, Hui Ji	The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i.e. separate the rain layer and the de-rained image layer from an rainy image.
381	Mutual-Structure for Joint Filtering	Xiaoyong Shen, Chao Zhou, Li Xu, Jiaya Jia	To address this issue, we propose the concept of mutual-structure, which refers to the structural information that is contained in both images and thus can be safely enhanced by joint filtering, and an untraditional objective function that can be efficiently optimized to yield mutual structure.
382	Photometric Stereo in a Scattering Medium	Zak Murez, Tali Treibitz, Ravi Ramamoorthi, David Kriegman	We measure the object point-spread function and introduce a simple deconvolution method.
383	Resolving Scale Ambiguity Via XSlit Aspect Ratio Analysis	Wei Yang, Haiting Lin, Sing Bing Kang, Jingyi Yu	In this paper, we show that alternative non-perspective cameras such as the crossed-slit or XSlit cameras exhibit a different depth-dependent aspect ratio (DDAR) property that can be used to 3D recovery.
384	Single-Shot Specular Surface Reconstruction With Gonio-Plenoptic Imaging	Lingfei Meng, Liyang Lu, Noah Bedard, Kathrin Berkner	We present a gonio-plenoptic imaging system that realizes a single-shot shape measurement for specular surfaces.
385	TransCut: Transparent Object Segmentation From a Light-Field Image	Yichao Xu, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi	We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image.
386	Depth Recovery From Light Field Using Focal Stack Symmetry	Haiting Lin, Can Chen, Sing Bing Kang, Jingyi Yu	We describe a technique to recover depth from a light field (LF) using two proposed features of the LF focal stack.
387	Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution	W. Williem, Ramesh Raskar, In Kyu Park	In this paper, we present a joint iterative anaglyph stereo matching and colorization framework for obtaining a set of disparity maps and colorized images.
388	Learning Data-Driven Reflectance Priors for Intrinsic Image Decomposition	Tinghui Zhou, Philipp Krahenbuhl, Alexei A. Efros	We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image.
389	Photometric Stereo With Small Angular Variations	Jian Wang, Yasuyuki Matsushita, Boxin Shi, Aswin C. Sankaranarayanan	We explore both theoretical justification and practical issues in the design of a compact and portable photometric stereo device on which a camera is surrounded by a ring of point light sources.
390	Occlusion-Aware Depth Estimation Using Light-Field Cameras	Ting-Chun Wang, Alexei A. Efros, Ravi Ramamoorthi	Recent work has demonstrated practical methods for passive depth estimation from light-field images.
391	Oriented Light-Field Windows for Scene Flow	Pratul P. Srinivasan, Michael W. Tao, Ren Ng, Ravi Ramamoorthi	In this paper, we leverage the recent use of light-field cameras to propose alternative – oriented light-field windows that enable more robust and accurate pixel comparisons.
392	Extended Depth of Field Catadioptric Imaging Using Focal Sweep	Ryunosuke Yokoya, Shree K. Nayar	In this paper, we use focal sweep to extend the DOF of a catadioptric imaging system.
393	Intrinsic Depth: Improving Depth Transfer With Intrinsic Images	Naejin Kong, Michael J. Black	We formulate the estimation of dense depth maps from video sequences as a problem of intrinsic image estimation.
394	Separating Fluorescent and Reflective Components by Using a Single Hyperspectral Image	Yinqiang Zheng, Ying Fu, Antony Lam, Imari Sato, Yoichi Sato	This paper introduces a novel method to separate fluorescent and reflective components in the spectral domain.
395	Frequency-Based Environment Matting by Compressive Sensing	Yiming Qian, Minglun Gong, Yee-Hong Yang	In this paper, we propose a novel approach to capturing and extracting the matte of a real scene effectively and efficiently.
396	Complementary Sets of Shutter Sequences for Motion Deblurring	Hae-Gon Jeon, Joon-Young Lee, Yudeog Han, Seon Joo Kim, In So Kweon	In this paper, we present a novel multi-image motion deblurring method utilizing the coded exposure technique.
397	Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior	Lei Zhang, Wei Wei, Yanning Zhang, Fei Li, Chunhua Shen, Qinfeng Shi	To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study.
398	A Gaussian Process Latent Variable Model for BRDF Inference	Stamatios Georgoulis, Vincent Vanweddingen, Marc Proesmans, Luc Van Gool	In this paper we address the problem of inferring higher order reflectance information starting from the minimal input of a single BRDF slice.
399	Active One-Shot Scan for Wide Depth Range Using a Light Field Projector Based on Coded Aperture	Hiroshi Kawasaki, Satoshi Ono, Yuki Horita, Yuki Shiba, Ryo Furukawa, Shinsaku Hiura	In the paper, we solve the problems by introducing a light field projector, which can project a depth-dependent pattern.
400	Model-Based Tracking at 300Hz Using Raw Time-of-Flight Observations	Jan Stuhmer, Sebastian Nowozin, Andrew Fitzgibbon, Richard Szeliski, Travis Perry, Sunil Acharya, Daniel Cremers, Jamie Shotton	In this paper, we show how to perform model-based object tracking which allows to reconstruct the object’s depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera.
401	Hyperspectral Super-Resolution by Coupled Spectral Unmixing	Charis Lanaras, Emmanuel Baltsavias, Konrad Schindler	In this paper, we propose a method which performs hyperspectral super-resolution by jointly unmixing the two input images into the pure reflectance spectra of the observed materials and the associated mixing coefficients.
402	Depth Selective Camera: A Direct, On-Chip, Programmable Technique for Depth Selectivity in Photography	Ryuichi Tadano, Adithya Kumar Pediredla, Ashok Veeraraghavan	In this paper, we show that such correlational sensors can also be used to selectively accept or reject light rays from certain scene depths.
403	A Groupwise Multilinear Correspondence Optimization for 3D Faces	Timo Bolkart, Stefanie Wuhrer	Inspired by the minimum description length approach, we propose the first method to jointly optimize a multilinear model and the registration of the 3D scans used for training.
404	Selective Encoding for Recognizing Unreliably Localized Faces	Ang Li, Vlad Morariu, Larry S. Davis	We propose a selective encoding framework which injects relevance information (e.g., foreground/background probabilities) into each cluster of a descriptor codebook.
405	Confidence Preserving Machine for Facial Action Unit Detection	Jiabei Zeng, Wen-Sheng Chu, Fernando De la Torre, Jeffrey F. Cohn, Zhang Xiong	To address the ubiquity of error, we propose a Confident Preserving Machine (CPM) that follows an easy-to-hard classification strategy.
406	Learning Social Relation Traits From Face Images	Zhanpeng Zhang, Ping Luo, Chen-Change Loy, Xiaoou Tang	Motivated by psychological studies, we investigate if such fine grained and high-level relation traits can be characterised and quantified from face images in the wild.
407	Robust Heart Rate Measurement From Video Using Select Random Patches	Antony Lam, Yoshinori Kuno	We present conditions under which cardiac activity extraction from local regions of the face can be treated as a linear Blind Source Separation problem and propose a simple but robust algorithm for selecting good local regions.
408	Robust Model-Based 3D Head Pose Estimation	Gregory P. Meyer, Shalini Gupta, Iuri Frosio, Dikpal Reddy, Jan Kautz	We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera.
409	Robust Facial Landmark Detection Under Significant Head Poses and Occlusion	Yue Wu, Qiang Ji	In this work, we propose a unified robust cascade regression framework that can handle both images with severe occlusion and images with large head poses.
410	Conditional Convolutional Neural Network for Modality-Aware Face Recognition	Chao Xiong, Xiaowei Zhao, Danhang Tang, Karlekar Jayashree, Shuicheng Yan, Tae-Kyun Kim	We propose a conditional Convolutional Neural Network, named as c-CNN, to handle multimodal face recognition.
411	From Facial Parts Responses to Face Detection: A Deep Learning Approach	Shuo Yang, Ping Luo, Chen-Change Loy, Xiaoou Tang	In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW.
412	Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification	Shengcai Liao, Stan Z. Li	To address the above issues, we derive a logistic metric learning approach with the PSD constraint and an asymmetric sample weighting strategy.
413	Pose-Invariant 3D Face Alignment	Amin Jourabloo, Xiaoming Liu	In order to address these limitations, this paper proposes a novel face alignment algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for a face image with an arbitrary pose.
414	From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning	Adria Ruiz, Joost Van de Weijer, Xavier Binefa	In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers.
415	Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face	Jungseock Joo, Francis F. Steen, Song-Chun Zhu	In this paper, we study a fully automated system that can infer the perceived traits of a person from his face — social dimensions, such as “intelligence,” “honesty,” and “competence” — and how those traits can be used to predict the outcomes of real-world social events that involve long-term commitments, such as political elections, job hires, and marriage engagements.
416	Simultaneous Local Binary Feature Learning and Encoding for Face Recognition	Jiwen Lu, Venice Erin Liong, Jie Zhou	In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) method for face recognition.
417	Deep Learning Face Attributes in the Wild	Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang	We propose a novel deep learning framework for attribute prediction in the wild.
418	Multi-Task Learning With Low Rank Attribute Embedding for Person Re-Identification	Chi Su, Fan Yang, Shiliang Zhang, Qi Tian, Larry S. Davis, Wen Gao	We propose a novel Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) framework for person re-identification.
419	Regressing a 3D Face Shape From a Single Image	Sergey Tulyakov, Nicu Sebe	In this work we present a method to estimate a 3D face shape from a single image.
420	Rendering of Eyes for Eye-Shape Registration and Gaze Estimation	Erroll Wood, Tadas Baltrusaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, Andreas Bulling	We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time.
421	Multi-Scale Learning for Low-Resolution Person Re-Identification	Xiang Li, Wei-Shi Zheng, Xiaojuan Wang, Tao Xiang, Shaogang Gong	To solve this LR person re-id problem, we propose a novel joint multi-scale learning framework, termed joint multi-scale discriminant component analysis (JUDEA).
422	Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection	Timur Almaev, Brais Martinez, Michel Valstar	In this article we explore the problem of constructing person-specific models for the detection of facial Action Units (AUs), addressing the problem from the point of view of Transfer Learning and Multi-Task Learning.
423	Pairwise Conditional Random Forests for Facial Expression Recognition	Arnaud Dapogny, Kevin Bailly, Severine Dubuisson	In this paper, we propose to learn Random Forests from heterogeneous derivative features (e.g. facial fiducial point movements or texture variations) upon pairs of images.
424	Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection	Stefanos Eleftheriadis, Ognjen Rudovic, Maja Pantic	We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units.
425	Leveraging Datasets With Varying Annotations for Face Alignment via Deep Regression Network	Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen	In this work, we propose a deep regression network coupled with sparse shape regression (DRN-SSR) to predict the union of all types of landmarks by leveraging datasets with varying annotations, each dataset with one type of annotation.
426	A Spatio-Temporal Appearance Representation for Viceo-Based Pedestrian Re-Identification	Kan Liu, Bingpeng Ma, Wei Zhang, Rui Huang	In this paper we consider the temporal alignment problem, in addition to the spatial one, and propose a new approach that takes the video of a walking person as input and builds a spatio-temporal appearance representation for pedestrian re-identification.
427	Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction	Yan Li, Ruiping Wang, Haomiao Liu, Huajie Jiang, Shiguang Shan, Xilin Chen	For this purpose, we propose a novel binary code learning framework by jointly encoding identity discriminability and a number of facial attributes into unified binary code.
428	An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model	Zijing Zhao, Kumar Ajay	This paper proposes a novel and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination, which provides strong feasibility for applications in surveillance, forensics and the search for missing children, etc.
429	Discriminative Pose-Free Descriptors for Face and Object Matching	Soubhik Sanyal, Sivaram Prasad Mudunuri, Soma Biswas	In this paper, we propose a discriminative pose-free descriptor (DPFD) which can be used to match faces/objects across pose variations.
430	Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation	Meina Kan, Shiguang Shan, Xilin Chen	To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE).
431	Regressive Tree Structured Model for Facial Landmark Localization	Gee-Sern Hsu, Kai-Hsiang Chang, Shih-Chieh Huang	We propose the Regressive Tree Structure Model (RTSM) to improve the run-time speed and localization accuracy.
432	Person Recognition in Personal Photo Collections	Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele	We propose a convnet based person recognition system on which we provide an in-depth analysis of informativeness of different body cues, impact of training data, and the common failure modes of the system.
433	Robust Statistical Face Frontalization	Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic	In this paper, we propose a novel method for joint frontal view reconstruction and landmark localization using a small set of frontal images only.
434	PIEFA: Personalized Incremental and Ensemble Face Alignment	Xi Peng, Shaoting Zhang, Yu Yang, Dimitris N. Metaxas	To address these limitations, we propose to exploit incremental learning for personalized ensemble alignment.
435	Understanding Everyday Hands in Action From RGB-D Images	Gregory Rogez, James S. Supancic III, Deva Ramanan	We introduce a large dataset of 12000 RGB-D images covering 71 everyday grasps in natural interactions.
436	Example-Based Modeling of Facial Texture From Deficient Data	Arnaud Dessein, William A. P. Smith, Richard C. Wilson, Edwin R. Hancock	We present an approach to modeling ear-to-ear, high-quality texture from one or more partial views of a face with possibly poor resolution and noise.
437	Learning to Predict Saliency on Face Images	Mai Xu, Yun Ren, Zulin Wang	This paper proposes a novel method, which learns to detect saliency of face images. To be more specific, we obtain a database of eye tracking over extensive face images, via conducting an eye tracking experiment.
438	Group Membership Prediction	Ziming Zhang, Yuting Chen, Venkatesh Saligrama	In this context we propose a novel probability model and introduce latent view-specific and view-shared random variables to jointly account for the view-specific appearance and cross-view similarities among data instances.
439	Extraction of Virtual Baselines From Distorted Document Images Using Curvilinear Projection	Gaofeng Meng, Zuming Huang, Yonghong Song, Shiming Xiang, Chunhong Pan	In this paper, we propose an efficient method for accurate extraction of these virtual visual cues from a curved document image.
440	Robust RGB-D Odometry Using Point and Line Features	Yan Lu, Dezhen Song	In experiments we compare our method with state-of-the-art methods including a keypoint-based approach and a dense visual odometry algorithm.
441	Learning a Discriminative Model for the Perception of Realism in Composite Images	Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, Alexei A. Efros	In this work, we are answering this question from a data-driven perspective by learning the perception of visual realism directly from large amounts of data.
442	What Makes Tom Hanks Look Like Tom Hanks	Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman	We demonstrate convincing results on a large variety of celebrities derived from Internet imagery and video.
443	Wide-Area Image Geolocalization With Aerial Reference Imagery	Scott Workman, Richard Souvenir, Nathan Jacobs	We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images. To support training these networks, we introduce a massive database that contains pairs of aerial and ground-level images from across the United States.
444	Personalized Age Progression With Aging Dictionary	Xiangbo Shu, Jinhui Tang, Hanjiang Lai, Luoqi Liu, Shuicheng Yan	In this paper, we aim to automatically render aging faces in a personalized way.
445	FaceDirector: Continuous Control of Facial Performance in Video	Charles Malleson, Jean-Charles Bazin, Oliver Wang, Derek Bradley, Thabo Beeler, Adrian Hilton, Alexander Sorkine-Hornung	We present a method to continuously blend between multiple facial performances of an actor, which can contain different facial expressions or emotional states.
446	Synthesizing Illumination Mosaics From Internet Photo-Collections	Dinghuang Ji, Enrique Dunn, Jan-Michael Frahm	We propose a framework for the automatic creation of time-lapse mosaics of a given scene.
447	Hot or Not: Exploring Correlations Between Appearance and Temperature	Daniel Glasner, Pascal Fua, Todd Zickler, Lihi Zelnik-Manor	In this paper we explore interactions between the appearance of an outdoor scene and the ambient temperature.
448	SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs	Yu Li, Dongbo Min, Michael S. Brown, Minh N. Do, Jiangbo Lu	This paper proposes a novel algorithm called sped-up PMBP (SPM-BP) to tackle this critical computational bottleneck and speeds up PMBP by 50-100 times.
449	Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation	Christian Bailer, Bertram Taetz, Didier Stricker	In this paper we present a dense correspondence field approach that is much less outlier prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields.
450	Dense Semantic Correspondence Where Every Pixel is a Classifier	Hilton Bristow, Jack Valmadre, Simon Lucey	We pose the correspondence problem as a graphical model, where the unary potentials are computed via convolution with the set of exemplar classifiers, and the joint potentials enforce smoothly varying correspondence assignment.
451	Multi-Image Matching via Fast Alternating Minimization	Xiaowei Zhou, Menglong Zhu, Kostas Daniilidis	In this paper we propose a global optimization-based approach to jointly matching a set of images.
452	Differential Recurrent Neural Networks for Action Recognition	Vivek Veeriah, Naifan Zhuang, Guo-Jun Qi	To address this problem, we propose a differential gating scheme for the LSTM neural network, which emphasizes on the change in information gain caused by the salient motions between the successive frames.
453	Similarity Gaussian Process Latent Variable Model for Multi-Modal Data Analysis	Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian	In this paper, we build our work based on Gaussian process latent variable model (GPLVM) to learn the non-linear non-parametric mapping functions and transform heterogeneous data into a shared latent space.
454	Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables	Hossein Hajimirsadeghi, Greg Mori	This paper presents HCRF-Boost, a novel and general framework for learning HCRFs in functional space.
455	Simultaneous Deep Transfer Across Domains and Tasks	Eric Tzeng, Judy Hoffman, Trevor Darrell, Kate Saenko	We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data.
456	Low Dimensional Explicit Feature Maps	Ondrej Chum	We propose a novel method of data independent construction of low dimensional feature maps.
457	Unsupervised Learning of Spatiotemporally Coherent Metrics	Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun	In this work we study unsupervised feature learning with convolutional networks in the context of temporally coherent unlabeled data.
458	Multi-Label Cross-Modal Retrieval	Viresh Ranjan, Nikhil Rasiwasia, C. V. Jawahar	In this work, we address the problem of cross-modal retrieval in presence of multi-label annotations.
459	Improving Ferns Ensembles by Sparsifying and Quantising Posterior Probabilities	Antonio L. Rodriguez, Vitor Sequeira	We introduce a two-fold contribution that produces large reductions in their memory consumption.
460	Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs	Mehrtash Harandi, Mathieu Salzmann, Mahsa Baktashmotlagh	Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators.
461	Unsupervised Domain Adaptation With Imbalanced Cross-Domain Data	Tzu Ming Harry Hsu, Wei Yu Chen, Cheng-An Hou, Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank Wang	To address the aforementioned settings of imbalanced cross-domain data, we propose Closest Common Space Learning (CCSL) for associating such data with the capability of preserving label and structural information within and across domains.
462	Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts	Je Hyeong Hong, Andrew Fitzgibbon	This paper provides a unified derivation of a number of recent approaches, so that similarities and differences are easily observed.
463	Geometry-Aware Deep Transform	Jiaji Huang, Qiang Qiu, Robert Calderbank, Guillermo Sapiro	In this paper, we propose a novel deep learning objective formulation that unifies both the classification and metric learning criteria.
464	Learning Binary Codes for Maximum Inner Product Search	Fumin Shen, Wei Liu, Shaoting Zhang, Yang Yang, Heng Tao Shen	In this paper, we investigate learning binary codes to exclusively handle the MIPS problem.
465	ML-MG: Multi-Label Learning With Missing Labels Using a Mixed Graph	Baoyuan Wu, Siwei Lyu, Bernard Ghanem	To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges.
466	Zero-Shot Learning via Semantic Similarity Embedding	Ziming Zhang, Venkatesh Saligrama	In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided.
467	Bayesian Model Adaptation for Crowd Counts	Bo Liu, Nuno Vasconcelos	A solution based on Bayesian model adaptation of Gaussian processes is proposed.
468	An NMF Perspective on Binary Hashing	Lopamudra Mukherjee, Sathya N. Ravi, Vamsi K. Ithapu, Tyler Holmes, Vikas Singh	We give a probabilistic analysis of our initialization scheme and present a range of experiments to show that the method is simple to implement and competes favorably with available methods (both for optimization and generalization).
469	Multi-View Domain Generalization for Visual Recognition	Li Niu, Wen Li, Dong Xu	In this paper, we propose a new multi-view domain generalization (MVDG) approach for visual recognition, in which we aim to use the source domain samples with multiple types of features (i.e., multi-view features) to learn robust classifiers that can generalize well to any unseen target domain.
470	Infinite Feature Selection	Giorgio Roffo, Simone Melzi, Marco Cristani	In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of infinite feature selection (Inf-FS).
471	Semi-Supervised Zero-Shot Classification With Label Representation Learning	Xin Li, Yuhong Guo, Dale Schuurmans	In this paper, we propose a novel zero-shot classification approach that automatically learns label embeddings from the input data in a semi-supervised large-margin learning framework.
472	A Supervised Low-Rank Method for Learning Invariant Subspaces	Farzad Siyahjani, Ranya Almohsen, Sinan Sabri, Gianfranco Doretto	We introduce the invariant components, a discriminative representation invariant to nuisance factors, because it spans subspaces orthogonal to the space where nuisance factors are defined.
473	Recursive Frechet Mean Computation on the Grassmannian and its Applications to Computer Vision	Rudrasis Chakraborty, Baba C. Vemuri	In this paper, we propose one such computationally efficient algorithm called the it Grassmann inductive Frechet mean estimator (GiFME).
474	Multi-View Subspace Clustering	Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang	In this paper, we propose a novel multi-view subspace clustering method.
475	Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions	Jimmy Lei Ba, Kevin Swersky, Sanja Fidler, Ruslan salakhutdinov	We present a new model that can classify unseen categories from their textual description.
476	Structured Feature Selection	Tian Gao, Ziheng Wang, Qiang Ji	2) We propose a method for structured feature selection to handle hierarchical features and show the proposed method can lead to big performance gain in facial expression and action unit (AU) recognition tasks.
477	Conditional High-Order Boltzmann Machine: A Supervised Learning Model for Relation Learning	Yan Huang, Wei Wang, Liang Wang	In this paper, we explore supervised learning algorithms and propose a new model named Conditional High-order Boltzmann Machine (CHBM), which can be directly used as a bilinear classifier to assign similarity scores for pairwise images.
478	Learning Image and User Features for Recommendation in Social Networks	Xue Geng, Hanwang Zhang, Jingwen Bian, Tat-Seng Chua	To address the challenges, we propose a novel deep model which learns the unified feature representations for both users and images.
479	Dual-Feature Warping-Based Motion Model Estimation	Shiwei Li, Lu Yuan, Jian Sun, Long Quan	In this paper we propose a simple and effective approach by considering both keypoint and line segment correspondences as data-term.
480	An Adaptive Data Representation for Robust Point-Set Registration and Merging	Dylan Campbell, Lars Petersson	This paper presents a framework for rigid point-set registration and merging using a robust continuous data representation.
481	Local Subspace Collaborative Tracking	Lin Ma, Xiaoqin Zhang, Weiming Hu, Junliang Xing, Jiwen Lu, Jie Zhou	To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances.
482	Learning Spatially Regularized Correlation Filters for Visual Tracking	Martin Danelljan, Gustav Hager, Fahad Shahbaz Khan, Michael Felsberg	We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking.
483	SpeDo: 6 DOF Ego-Motion Sensor Using Speckle Defocus Imaging	Kensei Jo, Mohit Gupta, Shree K. Nayar	We present a novel ego-motion sensor called SpeDo that addresses these fundamental limitations.
484	Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage	Hongteng Xu, Yang Zhou, Weiyao Lin, Hongyuan Zha	This paper proposes a shrinkage-based framework for unsupervised trajectory clustering.
485	TRIC-track: Tracking by Regression With Incrementally Learned Cascades	Xiaomeng Wang, Michel Valstar, Brais Martinez, Muhammad Haris Khan, Tony Pridmore	This paper proposes a novel approach to part-based tracking by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations.
486	Recurrent Network Models for Human Dynamics	Katerina Fragkiadaki, Sergey Levine, Panna Felsen, Jitendra Malik	We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture.
487	Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment	Huijun Di, Qingxuan Shi, Feng Lv, Ming Qin, Yao Lu	Our goal is to estimate contour flow (the contour pairs with consistent point correspondence) from inconsistent contours extracted independently in two video frames.
488	FollowMe: Efficient Online Min-Cost Flow Tracking With Bounded Memory and Computation	Philip Lenz, Andreas Geiger, Raquel Urtasun	In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution.
489	Learning to Divide and Conquer for Online Multi-Target Tracking	Francesco Solera, Simone Calderara, Rita Cucchiara	In this paper we claim that the ambiguities in tracking could be solved by a selective use of the features, by working with more reliable features if possible and exploiting a deeper representation of the target only if necessary.
490	Minimizing Human Effort in Interactive Tracking by Incremental Learning of Model Parameters	Arridhana Ciptadi, James M. Rehg	We address the problem of minimizing human effort in interactive tracking by learning sequence-specific model parameters.
491	A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images	Alberto Crivellaro, Mahdi Rad, Yannick Verdie, Kwang Moo Yi, Pascal Fua, Vincent Lepetit	We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object.
492	Linearization to Nonlinear Learning for Visual Tracking	Bo Ma, Hongwei Hu, Jianbing Shen, Yuping Zhang, Fatih Porikli	Building on the theory of globally linear approximations to nonlinear functions, we introduce an elegant method that jointly learns a nonlinear classifier and a visual dictionary for tracking objects in a semi-supervised sparse coding fashion.
493	Self-Occlusions and Disocclusions in Causal Video Object Segmentation	Yanchao Yang, Ganesh Sundaramoorthi, Stefano Soatto	We propose a method to detect disocclusion in video sequences of three-dimensional scenes and to partition the disoccluded regions into objects, defined by coherent deformation corresponding to surfaces in the scene.
494	Large Displacement 3D Scene Flow With Occlusion Reasoning	Andrei Zanfir, Cristian Sminchisescu	In this paper we propose a novel coarse to fine correspondence-based scene flow approach to account for the effects of large displacements and to model occlusion, based on explicit geometric reasoning.
495	Co-Interest Person Detection From Multiple Wearable Camera Videos	Yuewei Lin, Kareem Abdelfatah, Youjie Zhou, Xiaochuan Fan, Hongkai Yu, Hui Qian, Song Wang	In this paper, we tackle a new problem of locating the co-interest person (CIP), i.e., the one who draws attention from most camera wearers, from temporally synchronized videos taken by multiple wearable cameras. We collect three sets of wearable-camera videos for testing the proposed algorithm.
496	Sparse Dynamic 3D Reconstruction From Unsynchronized Videos	Enliang Zheng, Dinghuang Ji, Enrique Dunn, Jan-Michael Frahm	Our proposed compressed sensing framework poses the estimation of 3D structure as the problem of dictionary learning.
497	Category-Blind Human Action Recognition: A Practical Recognition System	Wenbo Li, Longyin Wen, Mooi Choo Chuah, Siwei Lyu	In this paper, we propose the category-blind human recognition method (CHARM) which can recognize a human action without making assumptions of the action category.
498	Temporal Subspace Clustering for Human Motion Segmentation	Sheng Li, Kang Li, Yun Fu	We propose a novel temporal subspace clustering (TSC) approach in this paper.
499	Weakly-Supervised Alignment of Video With Text	Piotr Bojanowski, Remi Lajugie, Edouard Grave, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid	We propose in this paper a method for aligning the two modalities, i.e., automatically providing a time (frame) stamp for every sentence.
500	Learning Temporal Embeddings for Complex Video Analysis	Vignesh Ramanathan, Kevin Tang, Greg Mori, Li Fei-Fei	In this paper, we propose to learn temporal embeddings of video frames for complex video analysis.
501	Unsupervised Semantic Parsing of Video Collections	Ozan Sener, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena	In this paper, we propose a method for parsing a video into such semantic steps in an unsupervised way.
502	Learning Spatiotemporal Features With 3D Convolutional Networks	Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri	We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
503	Temporal Perception and Prediction in Ego-Centric Video	Yipin Zhou, Tamara L. Berg	In this paper we explore two simple tasks related to temporal prediction in egocentric videos of everyday activities.
504	Describing Videos by Exploiting Temporal Structure	Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville	In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.
505	Person Re-Identification With Discriminatively Trained Viewpoint Invariant Dictionaries	Srikrishna Karanam, Yang Li, Richard J. Radke	This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view.
506	Storyline Representation of Egocentric Videos With an Applications to Story-Based Search	Bo Xiong, Gunhee Kim, Leonid Sigal	To address this, we propose a storyline representation that expresses an egocentric video as a set of jointly inferred, through MRF inference, story elements comprising of actors, locations, supporting objects and events, depicted on a timeline.
507	Sequence to Sequence – Video to Text	Subhashini Venugopalan, Marcus Rohrbach, Jeffrey Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko	To approach this problem we propose a novel end-to-end sequence-to-sequence model to generate captions for videos.
508	Context Aware Active Learning of Activity Recognition Models	Mahmudul Hasan, Amit K. Roy-Chowdhury	In contrast, we formulate a continuous learning framework for context aware activity recognition from unlabeled video data which has two distinct advantages over most existing methods.
509	Action Recognition by Hierarchical Mid-Level Action Elements	Tian Lan, Yuke Zhu, Amir Roshan Zamir, Silvio Savarese	We introduce an unsupervised method to generate this representation from videos.
510	Selecting Relevant Web Trained Concepts for Automated Event Retrieval	Bharat Singh, Xintong Han, Zhe Wu, Vlad I. Morariu, Larry S. Davis	We propose an event retrieval algorithm that constructs pairs of automatically discovered concepts and then prunes those concepts that are unlikely to be helpful for retrieval.
511	Beyond Covariance: Feature Representation With Nonlinear Kernel Matrices	Lei Wang, Jianjia Zhang, Luping Zhou, Chang Tang, Wanqing Li	It proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages.
512	Multiresolution Hierarchy Co-Clustering for Semantic Segmentation in Sequences With Small Variations	David Varas, Monica Alfaro, Ferran Marques	This paper presents a co-clustering technique that, given a collection of images and their hierarchies, clusters nodes from these hierarchies to obtain a coherent multiresolution representation of the image collection.
513	Objects2action: Classifying and Localizing Actions Without Any Video Example	Mihir Jain, Jan C. van Gemert, Thomas Mensink, Cees G. M. Snoek	The goal of this paper is to recognize actions in video without the need for examples.
514	Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks	Lin Sun, Kui Jia, Dit-Yan Yeung, Bertram E. Shi	This has triggered us to investigate in this paper a new deep architecture which can handle 3D signals more effectively.
515	Bayesian Non-Parametric Inference for Manifold Based MoCap Representation	Fabrizio Natola, Valsamis Ntouskos, Marta Sanzari, Fiora Pirri	We propose a novel approach to human action recognition, with motion capture data (MoCap), based on grouping sub-body parts.
516	Semantic Video Entity Linking Based on Visual Content and Metadata	Yuncheng Li, Xitong Yang, Jiebo Luo	In this paper, we propose to exploit video visual content to improve video entity linking.
517	Love Thy Neighbors: Image Annotation by Exploiting Image Metadata	Justin Johnson, Lamberto Ballan, Li Fei-Fei	We build on this intuition to improve multilabel image annotation.
518	Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-Encoders	Huan Yang, Baoyuan Wang, Stephen Lin, David Wipf, Minyi Guo, Baining Guo	Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised learning approach that takes advantage of the abundance of user-edited videos on social media websites such as YouTube.
519	Learning Visual Clothing Style With Heterogeneous Dyadic Co-Occurrences	Andreas Veit, Balazs Kovacs, Sean Bell, Julian McAuley, Kavita Bala, Serge Belongie	In this paper, we propose a novel learning framework to help answer these types of questions.
520	Text Flow: A Unified Text Detection System in Natural Scene Images	Shangxuan Tian, Yifeng Pan, Chang Huang, Shijian Lu, Kai Yu, Chew Lim Tan	To address these issues, we propose a unified scene text detection system, namely Text Flow, by utilizing the minimum cost (min-cost) flow network model.
521	Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos	Elisa Ricci, Jagannadan Varadarajan, Ramanathan Subramanian, Samuel Rota Bulo, Narendra Ahuja, Oswald Lanz	We present a novel approach for jointly estimating tar- gets’ head, body orientations and conversational groups called F-formations from a distant social scene (e.g., a cocktail party captured by surveillance cameras).
522	Generating Notifications for Missing Actions: Don’t Forget to Turn the Lights Off!	Bilge Soran, Ali Farhadi, Linda Shapiro	In this paper, we propose a solution to the problem of issuing notifications on actions that may be missed. In order to show a proof of concept, we collected a new egocentric dataset, in which people wear a camera while making lattes.
523	Partial Person Re-Identification	Wei-Shi Zheng, Xiang Li, Tao Xiang, Shengcai Liao, Jianhuang Lai, Shaogang Gong	To solve this more challenging and realistic re-id problem without the implicit assumption of manual body-parts alignment, we propose a matching framework consisting of 1) a local patch-level matching model based on a novel sparse representation classification formulation with explicit patch ambiguity modelling, and 2) a global part-based matching model providing complementary spatial layout information.
524	Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering With Corrupted and Incomplete Data	Pan Ji, Mathieu Salzmann, Hongdong Li	In this paper, we revisit the SIM and reveal its connections to several recent subspace clustering methods.
525	Multiple Hypothesis Tracking Revisited	Chanho Kim, Fuxin Li, Arridhana Ciptadi, James M. Rehg	In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis.
526	Learning to Track: Online Multi-Object Tracking by Decision Making	Yu Xiang, Alexandre Alahi, Silvio Savarese	In this work, we formulate the online MOT problem as decision making in Markov Decision Processes (MDPs), where the lifetime of an object is modeled with a MDP.