Paper Digest: CVPR 2014 Highlights

June 23, 2014October 6, 2019 admin

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) is one of the top computer vision conferences in the world. In 2014, it is to be held in Columbus, Ohio.

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: CVPR 2014 Papers

	Title	Authors	Highlight
1	Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction	Jian Cheng, Cong Leng, Jiaxiang Wu, Hainan Cui, Hanqing Lu	In this paper, we proposed a Cascade Hashing strategy to speed up the image matching.
2	Predicting Matchability	Wilfried Hartmann, Michal Havlena, Konrad Schindler	Here, we asked the question how to best decimate the list of interest points without losing matches, i.e. we aim to speed up matching by filtering out, in advance, those points which would not survive the matching stage.
3	Trinocular Geometry Revisited	Jean Ponce, Martial Hebert	Classical models of trinocular geometry based on the fundamental matrices and trifocal tensor associated with the corresponding cameras only provide partial answers to this fundamental question, in large part because of underlying, but seldom explicit, general configuration assumptions.
4	Critical Configurations For Radial Distortion Self-Calibration	Changchang Wu	In this paper, we study the configurations of motion and structure that lead to inherent ambiguities in radial distortion estimation (or 3D reconstruction with unknown radial distortions).
5	Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion	Yubin Kuang, Jan E. Solem, Fredrik Kahl, Kalle Astrom	In this paper, we study the problems of estimating relative pose between two cameras in the presence of radial distortion.
6	Reconstructing PASCAL VOC	Sara Vicente, Joao Carreira, Lourdes Agapito, Jorge Batista	We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations.
7	Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation	Fabio Galasso, Margret Keuper, Thomas Brox, Bernt Schiele	In this paper, we propose the use of a reduced graph based on superpixels.
8	Weakly Supervised Multiclass Video Segmentation	Xiao Liu, Dacheng Tao, Mingli Song, Ying Ruan, Chun Chen, Jiajun Bu	In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation.
9	Video Motion Segmentation Using New Adaptive Manifold Denoising Model	Dijun Luo, Heng Huang	We propose a novel motion segmentation approach for both rigid and non-rigid objects using adaptive manifold denoising.
10	Cut, Glue & Cut: A Fast, Approximate Solver for Multicut Partitioning	Thorsten Beier, Thorben Kroeger, Jorg H. Kappes, Ullrich Kothe, Fred A. Hamprecht	Since this problem is NP-hard, we propose a new approximate solver based on the move-making paradigm: first, the graph is recursively partitioned into small regions (cut phase).
11	Neural Decision Forests for Semantic Image Labelling	Samuel Rota Bulo, Peter Kontschieder	In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees.
12	Pulling Things out of Perspective	Lubor Ladicky, Jianbo Shi, Marc Pollefeys	In this paper, we show that we can use this property to reduce the learning of a pixel-wise depth classifier to a much simpler classifier predicting only the likelihood of a pixel being at an arbitrarily fixed canonical depth.
13	Event Detection using Multi-Level Relevance Labels and Multiple Features	Zhongwen Xu, Ivor W. Tsang, Yi Yang, Zhigang Ma, Alexander G. Hauptmann	In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning.
14	Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations	Stephan Liwicki, Minh-Tri Pham, Stefanos Zafeiriou, Maja Pantic, Bjorn Stenger	In this paper we introduce a new distance for robustly matching vectors of 3D rotations.
15	Semi-supervised Spectral Clustering for Image Set Classification	Arif Mahmood, Ajmal Mian, Robyn Owens	To this end, we propose an iterative sparse spectral clustering algorithm.
16	Look at the Driver, Look at the Road: No Distraction! No Accident!	Mahdi Rezaei, Reinhard Klette	The paper proposes an advanced driver-assistance system that correlates the driver’s head pose to road hazards by analyzing both simultaneously.
17	Measuring Distance Between Unordered Sets of Different Sizes	Andrew Gardner, Jinko Kanno, Christian A. Duncan, Rastko Selmic	We present a distance metric based upon the notion of minimum-cost injective mappings between sets.
18	Learning Mid-level Filters for Person Re-identification	Rui Zhao, Wanli Ouyang, Xiaogang Wang	In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification.
19	DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification	Wei Li, Rui Zhao, Tong Xiao, Xiaogang Wang	In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. We build the largest benchmark re-id dataset with 13,164 images of 1,360 pedestrians.
20	Lacunarity Analysis on Image Patterns for Texture Classification	Yuhui Quan, Yong Xu, Yuping Sun, Yu Luo	Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes.
21	Segmentation-aware Deformable Part Models	Eduard Trulls, Stavros Tsogkas, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer	In this work we propose a technique to combine bottom-up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs).
22	From Categories to Individuals in Real Time — A Unified Boosting Approach	David Hall, Pietro Perona	A method for online, real-time learning of individual-object detectors is presented.
23	NMF-KNN: Image Annotation using Weighted Multi-view Non-negative Matrix Factorization	Mahdi M. Kalayeh, Haroon Idrees, Mubarak Shah	In this paper, we present a weighted extension of Multi-view Non-negative Matrix Factorization (NMF) to address the aforementioned drawbacks.
24	Fine-Grained Visual Comparisons with Local Learning	Aron Yu, Kristen Grauman	To address these issues, we propose a local learning approach for fine-grained visual comparisons.
25	Inferring Analogous Attributes	Chao-Yeh Chen, Kristen Grauman	We propose a novel form of transfer learning that addresses this dilemma.
26	Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes	Lucy Liang, Kristen Grauman	We introduce a novel criterion that requests a partial ordering for a set of examples that minimizes the total rank margin in attribute space, subject to a visual diversity constraint.
27	Visual Persuasion: Inferring Communicative Intents of Images	Jungseock Joo, Weixin Li, Francis F. Steen, Song-Chun Zhu	In this paper we introduce the novel problem of understanding visual persuasion. To facilitate progress, we introduce a new dataset of 1,124 images of politicians labeled with ground-truth intents in the form of rankings.
28	Histograms of Pattern Sets for Image Classification and Object Recognition	Winn Voravuthikunchai, Bruno Cremilleux, Frederic Jurie	This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features.
29	Incorporating Scene Context and Object Layout into Appearance Modeling	Hamid Izadinia, Fereshteh Sadeghi, Ali Farhadi	In this paper, we propose a method to learn scene structures that can encode three main interlacing components of a scene: the scene category, the context-specific appearance of objects, and their layout.
30	Co-Segmentation of Textured 3D Shapes with Sparse Annotations	Mehmet Ersin Yumer, Won Chun, Ameesh Makadia	We present a novel co-segmentation method for textured 3D shapes.
31	How to Evaluate Foreground Maps?	Ran Margolin, Lihi Zelnik-Manor, Ayellet Tal	In this paper, we show that the most commonly-used measures for evaluating both non-binary maps and binary maps do not always provide a reliable evaluation.
32	MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation	Jiajun Wu, Yibiao Zhao, Jun-Yan Zhu, Siwei Luo, Zhuowen Tu	Here, we formulate the interactive segmentation problem as a multiple instance learning (MIL) task by generating positive bags from pixels of sweeping lines within a bounding box.
33	SCAMS: Simultaneous Clustering and Model Selection	Zhuwen Li, Loong-Fah Cheong, Steven Zhiying Zhou	Rather than adopting the conventional convex relaxation approach wholesale, we represent the original problem more faithfully by taking full advantage of the particular structure present in the optimization problem and solving it efficiently using the Alternating Direction Method of Multipliers.
34	The Shape-Time Random Field for Semantic Video Labeling	Andrew Kae, Benjamin Marlin, Erik Learned-Miller	We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to model both the shape and temporal dependencies of an object in video.
35	The Secrets of Salient Object Segmentation	Yin Li, Xiaodi Hou, Christof Koch, James M. Rehg, Alan L. Yuille	In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Based on our analysis, we propose a new high quality dataset that offers both fixation and salient object segmentation ground-truth.
36	Non-rigid Segmentation using Sparse Low Dimensional Manifolds and Deep Belief Networks	Jacinto C. Nascimento, Gustavo Carneiro	In this paper, we propose a new methodology for segmenting non-rigid visual objects, where the search procedure is onducted directly on a sparse low-dimensional manifold, guided by the classification results computed from a deep belief network.
37	An Exemplar-based CRF for Multi-instance Object Segmentation	Xuming He, Stephen Gould	Inspired by data-driven methods, we propose an exemplar-based approach to the task of instance segmentation, in which a set of reference image/shape masks is used to find multiple objects.
38	Object Partitioning using Local Convexity	Simon Christoph Stein, Markus Schoeler, Jeremie Papon, Florentin Worgotter	As an alternative to this, we present a new, efficient learning- and model-free approach for the segmentation of 3D point clouds into object parts.
39	Bayesian Active Contours with Affine-Invariant, Elastic Shape Prior	Darshan Bryner, Anuj Srivastava	This framework is demonstrated using a number of examples involving the segmentation of occluded or noisy images of targets subject to perspective skew.
40	Max-Margin Boltzmann Machines for Object Segmentation	Jimei Yang, Simon Safar, Ming-Hsuan Yang	We present Max-Margin Boltzmann Machines (MMBMs) for object segmentation.
41	Multiscale Combinatorial Grouping	Pablo Arbelaez, Jordi Pont-Tuset, Jonathan T. Barron, Ferran Marques, Jitendra Malik	We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG).
42	RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions	Ahmad Humayun, Fuxin Li, James M. Rehg	In this paper we propose an algorithm, RIGOR, for efficiently generating a pool of overlapping segment proposals in images.
43	Efficient Hierarchical Graph-Based Segmentation of RGBD Videos	Steven Hickson, Stan Birchfield, Irfan Essa, Henrik Christensen	We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach.
44	Point Matching in the Presence of Outliers in Both Point Sets: A Concave Optimization Approach	Wei Lian, Lei Zhang	Recently, a concave optimization approach has been proposed to solve the robust point matching (RPM) problem.
45	Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data	Feng-Ju Chang, Yen-Yu Lin, Kuang-Jui Hsu	We present an approach MSIL-CRF that incorporates multiple instance learning (MIL) into conditional random fields (CRFs).
46	Joint Motion Segmentation and Background Estimation in Dynamic Scenes	Adeel Mumtaz, Weichen Zhang, Antoni B. Chan	We propose a joint foreground-background mixture model (FBM) that simultaneously performs background estimation and motion segmentation in complex dynamic scenes. Since most dynamic scene datasets only contain videos with a single foreground object over a simple background, we develop a new challenging dataset with multiple foreground objects over complex dynamic backgrounds.
47	SeamSeg: Video Object Segmentation using Patch Seams	S. Avinash Ramakanth, R. Venkatesh Babu	In this paper, we propose a technique for video object segmentation using patch seams across frames.
48	Laplacian Coordinates for Seeded Image Segmentation	Wallace Casaca, Luis Gustavo Nonato, Gabriel Taubin	In this work we present a novel framework for seed-based image segmentation that is mathematically simple, easy to implement, and guaranteed to produce a unique solution.
49	Error-tolerant Scribbles Based Interactive Image Segmentation	Junjie Bai, Xiaodong Wu	In this paper, we propose a novel ratio energy function that tolerates errors in the user input while encouraging maximum use of the user input information.
50	Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI	Nagesh Subbanna, Doina Precup, Tal Arbel	In this paper, we introduce a fully automated multistage graphical probabilistic framework to segment brain tumours from multimodal Magnetic Resonance Images (MRIs) acquired from real patients.
51	Large Scale Multi-view Stereopsis Evaluation	Rasmus Jensen, Anders Dahl, George Vogiatzis, Engin Tola, Henrik Aanaes	Specifically, we propose a dataset containing 80 scenes of large variability.
52	Timing-Based Local Descriptor for Dynamic Surfaces	Tony Tung, Takashi Matsuyama	In this paper, we present the first local descriptor designed for dynamic surfaces.
53	A Minimal Solution to the Generalized Pose-and-Scale Problem	Jonathan Ventura, Clemens Arth, Gerhard Reitmayr, Dieter Schmalstieg	We propose a novel solution to the generalized camera pose problem which includes the internal scale of the generalized camera as an unknown parameter.
54	A General and Simple Method for Camera Pose and Focal Length Determination	Yinqiang Zheng, Shigeki Sugimoto, Imari Sato, Masatoshi Okutomi	In this paper, we revisit the pose determination problem of a partially calibrated camera with unknown focal length, hereafter referred to as the PnPf problem, by using n (n ≥ 4) 3D-to-2D point correspondences.
55	Partial Symmetry in Polynomial Systems and its Applications in Computer Vision	Yubin Kuang, Yinqiang Zheng, Kalle Astrom	We develop novel numerical schemes to utilize such partial symmetry.
56	Efficient Computation of Relative Pose for Multi-Camera Systems	Laurent Kneip, Hongdong Li	We present a novel solution to compute the relative pose of a generalized camera.
57	Simultaneous Localization and Calibration: Self-Calibration of Consumer Depth Cameras	Qian-Yi Zhou, Vladlen Koltun	We describe an approach for simultaneous localization and calibration of a stream of range images.
58	Minimal Scene Descriptions from Structure from Motion Models	Song Cao, Noah Snavely	In particular, we introduce a new method for computing compact models that takes into account both image-point relationships and feature distinctiveness, and we show that this method produces small models that yield better recognition performance than previous model reduction techniques.
59	Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels	Andras Bodis-Szomoru, Hayko Riemenschneider, Luc Van Gool	We present a novel approach for producing dense reconstructions from multiple images and from the underlying sparse Structure-from-Motion (SfM) data in an efficient way.
60	On Projective Reconstruction In Arbitrary Dimensions	Behrooz Nasihatkon, Richard Hartley, Jochen Trumpf	We present a theory whose point of departure is the projective equations rather than the Grassmann tensor.
61	Stereo under Sequential Optimal Sampling: A Statistical Analysis Framework for Search Space Reduction	Yilin Wang, Ke Wang, Enrique Dunn, Jan-Michael Frahm	We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model.
62	Efficient Pruning LMI Conditions for Branch-and-Prune Rank and Chirality-Constrained Estimation of the Dual Absolute Quadric	Adlane Habed, Danda Pani Paudel, Cedric Demonceaux, David Fofi	We present a new globally optimal algorithm for self-calibrating a moving camera with constant parameters.
63	Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection	Luis Ferraz, Xavier Binefa, Francesc Moreno-Noguer	We propose a real-time, robust to outliers and accurate solution to the Perspective-n-Point (PnP) problem.
64	Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains	Jose Lezama, Rafael Grompone von Gioi, Gregory Randall, Jean-Michel Morel	We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection.
65	Discriminative Feature-to-Point Matching in Image-Based Localization	Michael Donoser, Dieter Schmalstieg	In this paper we demonstrate that these sets contain useful information that can be exploited by formulating matching as a discriminative classification problem.
66	Two-View Camera Housing Parameters Calibration for Multi-Layer Flat Refractive Interface	Xida Chen, Yee-Hong Yang	In this paper, we present a novel refractive calibration method for an underwater stereo camera system where both cameras are looking through multiple parallel flat refractive interfaces.
67	Accurate Localization and Pose Estimation for Large 3D Models	Linus Svarm, Olof Enqvist, Magnus Oskarsson, Fredrik Kahl	In this paper we use recent theoretical as well as technical advances to tackle these problems.
68	Relative Pose Estimation for a Multi-Camera System with Known Vertical Direction	Gim Hee Lee, Marc Pollefeys, Friedrich Fraundorfer	In this paper, we present our minimal 4-point and linear 8-point algorithms to estimate the relative pose of a multi-camera system with known vertical directions, i.e. known absolute roll and pitch angles.
69	Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case	Sebastian Nowozin	In this work we investigate optimal decision making for more realistic loss functions.
70	Covariance Trees for 2D and 3D Processing	Thierry Guillemot, Andres Almansa, Tamy Boubekeur	Nevertheless, their adoption level was kept relatively low because of the computational cost associated to learning such models on large image databases.
71	Hierarchical Subquery Evaluation for Active Learning on a Graph	Oisin Mac Aodha, Neill D.F. Campbell, Jan Kautz, Gabriel J. Brostow	We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction.
72	Anytime Recognition of Objects and Scenes	Sergey Karayev, Mario Fritz, Trevor Darrell	We present a method for learning dynamic policies to optimize Anytime performance in visual architectures.
73	Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation	Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik	In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012—achieving a mAP of 53.3%.
74	Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group	Raviteja Vemulapalli, Felipe Arrate, Rama Chellappa	In this paper, we propose a new skeletal representation that explicitly models the 3D geometric relationships between various body parts using rotations and translations in 3D space.
75	Multi-View Super Vector for Action Recognition	Zhuowei Cai, Limin Wang, Xiaojiang Peng, Yu Qiao	In this paper, we propose a new global representation, Multi-View Super Vector (MVSV), which is composed of relatively independent components derived from a pair of descriptors.
76	Unsupervised Spectral Dual Assignment Clustering of Human Actions in Context	Simon Jones, Ling Shao	To solve this problem, we introduce a novel, general purpose algorithm, Dual Assignment k-Means (DAKM), which is uniquely capable of performing two co-occurring clustering tasks simultaneously, while exploiting the correlation information to enhance both clusterings.
77	Parsing Videos of Actions with Segmental Grammars	Hamed Pirsiavash, Deva Ramanan	We describe simple grammars that capture hierarchical temporal structure while admitting inference with a finite-state-machine.
78	Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition	Jingyong Su, Anuj Srivastava, Fillipe D. M. de Souza, Sudeep Sarkar	We apply this framework to the problem of speech recognition using both audio and visual components.
79	Piecewise Planar and Compact Floorplan Reconstruction from Images	Ricardo Cabral, Yasutaka Furukawa	This paper presents a system to reconstruct piecewise planar and compact floorplans from images, which are then converted to high quality texture-mapped models for free- viewpoint visualization.
80	Data-driven Flower Petal Modeling with Botany Priors	Chenxi Zhang, Mao Ye, Bo Fu, Ruigang Yang	In this paper we focus on the 3D modeling of flower, in particular the petals.
81	User-Specific Hand Modeling from Monocular Depth Sequences	Jonathan Taylor, Richard Stebbing, Varun Ramakrishna, Cem Keskin, Jamie Shotton, Shahram Izadi, Aaron Hertzmann, Andrew Fitzgibbon	This paper presents a method for acquiring dense nonrigid shape and deformation from a single monocular depth sensor.
82	Class Specific 3D Object Shape Priors Using Surface Normals	Christian Hane, Nikolay Savinov, Marc Pollefeys	We argue that this problem can be solved by exploiting the object class specific local surface orientations, e.g. a car is always close to horizontal in the roof area.
83	Frequency-Based 3D Reconstruction of Transparent and Specular Objects	Ding Liu, Xida Chen, Yee-Hong Yang	We propose a frequency-based 3D reconstruction method, which incorporates the frequency-based matting method.
84	Human Body Shape Estimation Using a Multi-Resolution Manifold Forest	Frank Perbet, Sam Johnson, Minh-Tri Pham, Bjorn Stenger	This paper proposes a method for estimating the 3D body shape of a person with robustness to clothing.
85	Quality Dynamic Human Body Modeling Using a Single Low-cost Depth Camera	Qing Zhang, Bo Fu, Mao Ye, Ruigang Yang	In this paper we present a novel autonomous pipeline to build a personalized parametric model (pose-driven avatar) using a single depth sensor.
86	Single-View 3D Scene Parsing by Attributed Grammar	Xiaobai Liu, Yibiao Zhao, Song-Chun Zhu	In this paper, we present an attributed grammar for parsing man-made outdoor scenes into semantic surfaces, and recovering its 3D model simultaneously.
87	Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction	Changqing Zou, Heng Yang, Jianzhuang Liu	We propose an effective method to conduct the line drawing separation and turn a complex line drawing into parametric 3D models.
88	When 3D Reconstruction Meets Ubiquitous RGB-D Images	Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki	In this paper, we propose to learn 3D reconstruction knowledge from informally captured RGB-D images, which will probably be ubiquitously used in daily life.
89	Stable Template-Based Isometric 3D Reconstruction in All Imaging Conditions by Linear Least-Squares	Ajad Chhatkuli, Daniel Pizarro, Adrien Bartoli	We here bring answers to what is theoretically recoverable in such imaging conditions, and explain why existing convex numerical solutions and analytical solutions to 3D reconstruction may be unstable.
90	Discrete-Continuous Depth Estimation from a Single Image	Miaomiao Liu, Mathieu Salzmann, Xuming He	In this paper, we tackle the problem of estimating the depth of a scene from a single image.
91	Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition	Di Wu, Ling Shao	We propose a hierarchial dynamic framework that first extracts high level skeletal joints features and then uses the learned representation for estimating emission probability to infer action sequences.
92	Seeing What You’re Told: Sentence-Guided Activity Recognition In Video	Narayanaswamy Siddharth, Andrei Barbu, Jeffrey Mark Siskind	We present a system that demonstrates how the compositional structure of events, in concert with the compositional structure of language, can interplay with the underlying focusing mechanisms in video action recognition, providing a medium for top-down and bottom-up integration as well as multi-modal integration between vision and language.
93	Action Localization with Tubelets from Motion	Mihir Jain, Jan van Gemert, Herve Jegou, Patrick Bouthemy, Cees G.M. Snoek	We introduce a sampling strategy to produce 2D+t sequences of bounding boxes, called tubelets.
94	Actionness Ranking with Lattice Conditional Ordinal Random Fields	Wei Chen, Caiming Xiong, Ran Xu, Jason J. Corso	To solve the general problem, we propose the lattice conditional ordinal random field model that incorporates local evidence as well as neighboring order agreement.
95	Multiple Granularity Analysis for Fine-grained Action Detection	Bingbing Ni, Vignesh R. Paramathayalan, Pierre Moulin	Knowing that the major challenge is frequent mutual occlusions during manipulation, we propose an “interaction tracking” framework in which hand/object position and interaction status are jointly tracked by explicitly modeling the contextual information between mutual occlusion and interaction status.
96	Human Action Recognition Across Datasets by Foreground-weighted Histogram Decomposition	Waqas Sultani, Imran Saleemi	This paper attempts to address the problem of recognizing human actions while training and testing on distinct datasets, when test videos are neither labeled nor available during training.
97	Range-Sample Depth Feature for Action Recognition	Cewu Lu, Jiaya Jia, Chi-Keung Tang	We propose binary range-sample feature in depth.
98	The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities	Hilde Kuehne, Ali Arslan, Thomas Serre	This paper describes a framework for modeling human activities as temporally structured processes. To evaluate our approach, we collected a large dataset of daily cooking activities: The dataset includes a total of 52 participants, each performing a total of 10 cooking activities in multiple real-life kitchens, resulting in over 77 hours of video footage.
99	Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video	Eran Swears, Anthony Hoogs, Qiang Ji, Kim Boyer	We propose a novel structure learning solution that fuses the Granger Causality statistic, a direct measure of temporal dependence, with the Adaboost feature selection algorithm to automatically constrain the temporal links of a DBN in a discriminative manner.
100	Incremental Activity Modeling and Recognition in Streaming Videos	Mahmudul Hasan, Amit K. Roy-Chowdhury	In this work, we develop an incremental activity learning framework that is able to continuously update the activity models and learn new ones as more videos are seen.
101	Super Normal Vector for Activity Recognition Using Depth Sequences	Xiaodong Yang, YingLi Tian	This paper presents a new framework for human activity recognition from video sequences captured by a depth camera.
102	Discriminative Hierarchical Modeling of Spatio-Temporally Composable Human Activities	Ivan Lillo, Alvaro Soto, Juan Carlos Niebles	This paper proposes a framework for recognizing complex human activities in videos. To evaluate the effectiveness of our proposed framework, we introduce a new dataset of composed human activities.
103	A Multigraph Representation for Improved Unsupervised/Semi-supervised Learning of Human Actions	Simon Jones, Ling Shao	Accordingly, we present a new spectral method – the Feature Grouped Spectral Multigraph (FGSM) – which comprises the following steps.
104	StoryGraphs: Visualizing Character Interactions as a Timeline	Makarand Tapaswi, Martin Bauml, Rainer Stiefelhagen	We present a novel way to automatically summarize and represent the storyline of a TV episode by visualizing character interactions as a chart.
105	Learning Receptive Fields for Pooling from Tensors of Feature Response	Can Xu, Nuno Vasconcelos	A new method for learning pooling receptive fields for recognition is presented.
106	Towards Unified Human Parsing and Pose Estimation	Jian Dong, Qiang Chen, Xiaohui Shen, Jianchao Yang, Shuicheng Yan	In this work, we propose a unified framework for simultaneous human parsing and pose estimation based on semantic parts. By utilizing Parselets and Mixture of Joint-Group Templates as the representations for these semantic parts, we seamlessly formulate the human parsing and pose estimation problem jointly within a unified framework via a tailored And-Or graph.
107	Ask the Image: Supervised Pooling to Preserve Feature Locality	Sean Ryan Fanello, Nicoletta Noceti, Carlo Ciliberto, Giorgio Metta, Francesca Odone	In this paper we propose a weighted supervised pooling method for visual recognition systems.
108	Similarity Comparisons for Interactive Fine-Grained Categorization	Catherine Wah, Grant Van Horn, Steve Branson, Subhransu Maji, Pietro Perona, Serge Belongie	In this work, we move away from that expert-driven and attribute-centric paradigm and present a novel interactive classification system that incorporates computer vision and perceptual similarity metrics in a unified framework.
109	Continuous Manifold Based Adaptation for Evolving Visual Domains	Judy Hoffman, Trevor Darrell, Kate Saenko	We formulate a novel problem of adapting to such continuous domains, and present a solution based on smoothly varying embeddings.
110	Talking Heads: Detecting Humans and Recognizing Their Interactions	Minh Hoai, Andrew Zisserman	The objective of this work is to accurately and efficiently detect configurations of one or more people in edited TV material.
111	Salient Region Detection via High-Dimensional Color Transform	Jiwhan Kim, Dongyoon Han, Yu-Wing Tai, Junmo Kim	In this paper, we introduce a novel technique to automatically detect salient regions of an image via high-dimensional color transform.
112	The Role of Context for Object Detection and Semantic Segmentation in the Wild	Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, Alan Yuille	In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches.
113	Switchable Deep Network for Pedestrian Detection	Ping Luo, Yonglong Tian, Xiaogang Wang, Xiaoou Tang	In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection.
114	Compact Representation for Image Classification: To Choose or to Compress?	Yu Zhang, Jianxin Wu, Jianfei Cai	We propose a supervised mutual information (MI) based importance sorting algorithm to choose features.
115	Capturing Long-tail Distributions of Object Subcategories	Xiangxin Zhu, Dragomir Anguelov, Deva Ramanan	We describe distributed algorithms for learning large- mixture models that capture long-tail distributions, which are hard to model with current approaches.
116	Accurate Object Detection with Joint Classification-Regression Random Forests	Samuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth, Horst Bischof	In this paper, we present a novel object detection approach that is capable of regressing the aspect ratio of objects.
117	Additive Quantization for Extreme Vector Compression	Artem Babenko, Victor Lempitsky	We introduce a new compression scheme for high-dimensional vectors that approximates the vectors using sums of M codewords coming from M different codebooks.
118	Product Sparse Coding	Tiezheng Ge, Kaiming He, Jian Sun	In this paper, we study a special case of sparse coding in which the codebook is a Cartesian product of two subcodebooks.
119	Informed Haar-like Features Improve Pedestrian Detection	Shanshan Zhang, Christian Bauckhage, Armin B. Cremers	We propose a simple yet effective detector for pedestrian detection.
120	Image Reconstruction from Bag-of-Visual-Words	Hiroharu Kato, Tatsuya Harada	The objective of this study is to reconstruct images from Bag-of-Visual-Words (BoVW), which is the de facto standard feature for image retrieval and recognition.
121	Beta Process Multiple Kernel Learning	Bingbing Ni, Teng Li, Pierre Moulin	To address this issue, we propose to automatically select good feature instances when calculating the kernel representation in multiple kernel learning.
122	Random Laplace Feature Maps for Semigroup Kernels on Histograms	Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney	Analogous to random Fourier feature maps to approximate shift-invariant kernels, such as the Gaussian kernel, we develop a new randomized technique called random Laplace features, to approximate a family of kernel functions adapted to the semigroup structure.
123	Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification	Yadong Mu, Gang Hua, Wei Fan, Shih-Fu Chang	This paper presents a novel algorithm which uses compact hash bits to greatly improve the efficiency of non-linear kernel SVM in very large scale visual classification problems.
124	Transitive Distance Clustering with K-Means Duality	Zhiding Yu, Chunjing Xu, Deyu Meng, Zhuo Hui, Fanyi Xiao, Wenbo Liu, Jianzhuang Liu	We propose a very intuitive and simple approximation for the conventional spectral clustering methods.
125	Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction	Chetan Tonde, Ahmed Elgammal	In this work, we propose a novel and efficient algorithm for learning kernel functions simultaneously, on both input and output domains.
126	Bregman Divergences for Infinite Dimensional Covariance Matrices	Mehrtash Harandi, Mathieu Salzmann, Fatih Porikli	We introduce an approach to computing and comparing Covariance Descriptors (CovDs) in infinite-dimensional spaces.
127	Optimizing Average Precision using Weakly Supervised Data	Aseem Behl, C. V. Jawahar, M. Pawan Kumar	In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples.
128	Subspace Clustering for Sequential Data	Stephen Tierney, Junbin Gao, Yi Guo	We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces.
129	Predicting Multiple Attributes via Relative Multi-task Learning	Lin Chen, Qiang Zhang, Baoxin Li	In this paper, we proposed a relative multi-attribute learning framework that integrates relative attributes into a multi-task learning scheme.
130	Learning Inhomogeneous FRAME Models for Object Patterns	Jianwen Xie, Wenze Hu, Song-Chun Zhu, Ying Nian Wu	We propose to select these locations, scales and orientations by a shared sparse coding scheme, and we explore the connection between the sparse FRAME model and the linear additive sparse coding model.
131	Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters	Vittal Premachandran, Daniel Tarlow, Dhruv Batra	In this work, we present a simple meta-algorithm that is surprisingly effective Empirical Min Bayes Risk.
132	Fantope Regularization in Metric Learning	Marc T. Law, Nicolas Thome, Matthieu Cord	This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning.
133	Kernel-PCA Analysis of Surface Normals for Shape-from-Shading	Patrick Snape, Stefanos Zafeiriou	We propose a kernel-based framework for computing components from a set of surface normals.
134	Merging SVMs with Linear Discriminant Analysis: A Combined Model	Symeon Nikitidis, Stefanos Zafeiriou, Maja Pantic	To remedy this problem, we propose a joint dimensionality reduction and classification framework by formulating an optimization problem within the maximum margin class separation task.
135	Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning	Bang Zhang, Yi Wang, Yang Wang, Fang Chen	Many prevalent multi-class classification approaches can be unified and generalized by the output coding framework which usually consists of three phases: (1) coding, (2) learning binary classifiers, and (3) decoding.
136	Finding the Subspace Mean or Median to Fit Your Need	Tim Marrinan, J. Ross Beveridge, Bruce Draper, Michael Kirby, Chris Peterson	Many computer vision algorithms employ subspace models to represent data.
137	Adaptive Color Attributes for Real-Time Visual Tracking	Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg, Joost van de Weijer	This paper investigates the contribution of color in a tracking-by-detection framework.
138	Local Layering for Joint Motion Estimation and Occlusion Detection	Deqing Sun, Ce Liu, Hanspeter Pfister	To handle such situations, we propose a local layering model where motion and occlusion relationships are inferred jointly.
139	Realtime and Robust Hand Tracking from Depth	Chen Qian, Xiao Sun, Yichen Wei, Xiaoou Tang, Jian Sun	We propose a hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy.
140	Multi-Output Learning for Camera Relocalization	Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, Shahram Izadi	We propose a hybrid discriminative-generative learning architecture that consists of: (i) a set of M predictors which generate M camera pose hypotheses; and (ii) a ‘selector’ or ‘aggregator’ that infers the best pose from the multiple pose hypotheses based on a similarity function.
141	MAP Visibility Estimation for Large-Scale Dynamic 3D Reconstruction	Hanbyul Joo, Hyun Soo Park, Yaser Sheikh	We present a maximum a posteriori (MAP) estimate of the time-varying visibility of the target points to reconstruct the 3D motion of an event from a large number of cameras.
142	Multi-Object Tracking via Constrained Sequential Labeling	Sheng Chen, Alan Fern, Sinisa Todorovic	This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations.
143	A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields	Alexander Fix, Chen Wang, Ramin Zabih	In this paper we propose a new primal-dual energy minimization method for arbitrary higher-order multilabel MRF’s.
144	Energy Based Multi-model Fitting & Matching for 3D Reconstruction	Hossam Isack, Yuri Boykov	We jointly solve feature matching and multi-model fitting problems by optimizing one energy.
145	Submodularization for Binary Pairwise Energies	Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong	We propose a general optimization framework based on local submodular approximations (LSA).
146	Maximum Persistency in Energy Minimization	Alexander Shekhovtsov	We propose a new sufficient condition for partial optimality which is: (1) verifiable in polynomial time (2) invariant to reparametrization of the problem and permutation of labels and (3) includes many existing sufficient conditions as special cases. We pose the problem of finding the maximum optimal partial assignment identifiable by the new sufficient condition.
147	Partial Optimality by Pruning for MAP-inference with General Graphical Models	Paul Swoboda, Bogdan Savchynskyy, Jorg H. Kappes, Christoph Schnorr	We propose a novel polynomial time algorithm to obtain a part of its optimal nonrelaxed integral solution.
148	Scene Labeling Using Beam Search Under Mutex Constraints	Anirban Roy, Sinisa Todorovic	This paper addresses the problem of assigning object class labels to image pixels.
149	Persistent Tracking for Wide Area Aerial Surveillance	Jan Prokaj, Gerard Medioni	We present a multiple target tracking approach that does not exclusively rely on background subtraction and is better able to track targets through stops.
150	Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation	Xiangyuan Lan, Andy J. Ma, Pong C. Yuen	To address this issue in multicue visual tracking, this paper proposes a new joint sparse representation model for robust feature-level fusion.
151	Multi-Forest Tracker: A Chameleon in Tracking	David J. Tan, Slobodan Ilic	In this paper, we address the problem of object tracking in intensity images and depth data.
152	Rigid Motion Segmentation using Randomized Voting	Heechul Jung, Jeongwoo Ju, Junmo Kim	In this paper, we propose a novel rigid motion segmentation algorithm called randomized voting (RV).
153	Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning	Seung-Hwan Bae, Kuk-Jin Yoon	In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively.
154	Pyramid-based Visual Tracking Using Sparsity Represented Mean Transform	Zhe Zhang, Kin Hong Wong	In this paper, we propose a robust method for visual tracking relying on mean shift, sparse coding and spatial pyramids.
155	Tracklet Association with Online Target-Specific Metric Learning	Bing Wang, Gang Wang, Kap Luk Chan, Li Wang	This paper presents a novel introduction of online target-specific metric learning in track fragment (tracklet) association by network flow optimization for long-term multi-person tracking.
156	An Online Learned Elementary Grouping Model for Multi-target Tracking	Xiaojing Chen, Zhen Qin, Le An, Bir Bhanu	We introduce an online approach to learn possible elementary groups (groups that contain only two targets) for inferring high level context that can be used to improve multi-target tracking in a data-association based framework.
157	Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking	Xiongbiao Luo, Ying Wan, Xiangjian He, Jie Yang, Kensaku Mori	The paper proposes a diversity-enhanced condensation algorithm to address the particle impoverishment problem which stochastic filtering usually suffers from.
158	Partial Occlusion Handling for Visual Tracking via Robust Part Matching	Tianzhu Zhang, Kui Jia, Changsheng Xu, Yi Ma, Narendra Ahuja	In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices.
159	Speeding Up Tracking by Ignoring Features	Lu Zhang, Hamdi Dibeklioglu, Laurens van der Maaten	To resolve this problem, the paper presents a new approach that limits the computational costs of trackers by ignoring features in image regions that — after inspecting a few features — are unlikely to contain the target object.
160	Subspace Tracking under Dynamic Dimensionality for Online Background Subtraction	Matthew Berger, Lee M. Seversky	This work proposes an online method for background modeling of dynamic point trajectories via tracking of a linear subspace describing the background motion.
161	Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph	Longyin Wen, Wenbo Li, Junjie Yan, Zhen Lei, Dong Yi, Stan Z. Li	In this paper, a novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed undirected affinity graph.
162	Bi-label Propagation for Generic Multiple Object Tracking	Wenhan Luo, Tae-Kyun Kim, Bjorn Stenger, Xiaowei Zhao, Roberto Cipolla	In this paper, we propose a label propagation framework to handle the multiple object tracking (MOT) problem for a generic object type (cf. pedestrian tracking).
163	A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions	Menglong Yang, Yiguang Liu, Longyin Wen, Zhisheng You, Stan Z. Li	This paper presents a novel probability framework for multitarget tracking with mutual occlusions.
164	Occlusion Geodesics for Online Multi-Object Tracking	Horst Possegger, Thomas Mauthner, Peter M. Roth, Horst Bischof	We address this problem by proposing an online approach based on the observation that object detectors primarily fail if objects are significantly occluded.
165	Efficient Nonlinear Markov Models for Human Motion	Andreas M. Lehrmann, Peter V. Gehler, Sebastian Nowozin	In this work we propose to instead use simple Markov models that only model observed quantities.
166	A Compositional Model for Low-Dimensional Image Set Representation	Hossein Mobahi, Ce Liu, William T. Freeman	We show that each component can be approximated by a low-dimensional subspace when the others are factored out.
167	A Principled Approach for Coarse-to-Fine MAP Inference	Christopher Zach	In this work we reconsider labeling problems with (virtually) continuous state spaces, which are of relevance in low level computer vision.
168	Fast Approximate Inference in Higher Order MRF-MAP Labeling Problems	Chetan Arora, Subhashis Banerjee, Prem Kalra, S.N. Maheshwari	In this paper we report an algorithm called Approximate Cuts (AC) which uses a generalization of the gadget of GC and provides an approximate solution to inference in 2-label MRF-MAP problems with cliques of size k ≥ 2.
169	Multi Label Generic Cuts: Optimal Inference in Multi Label Multi Clique MRF-MAP Problems	Chetan Arora, S.N. Maheshwari	We propose an algorithm called Multi Label Generic Cuts (MLGC) for computing optimal solutions to MRF-MAP problems with submodular multi label multi-clique potentials.
170	Scanline Sampler without Detailed Balance: An Efficient MCMC for MRF Optimization	Wonsik Kim, Kyoung Mu Lee	In experimental section, we apply our method to the OpenGM2 benchmark of MRF optimization and show the proposed method achieves faster convergence than the conventional approaches.
171	Higher-Order Clique Reduction Without Auxiliary Variables	Hiroshi Ishikawa	We introduce a method to reduce most higher-order terms of Markov Random Fields with binary labels into lower-order ones without introducing any new variables, while keeping the minimizer of the energy unchanged.
172	Topic Modeling of Multimodal Data: An Autoregressive Approach	Yin Zheng, Yu-Jin Zhang, Hugo Larochelle	In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation.
173	Model Transport: Towards Scalable Transfer Learning on Manifolds	Oren Freifeld, Soren Hauberg, Michael J. Black	We demonstrate the approach by transferring PCA and logistic-regression models of real-world data involving 3D shapes and image descriptors.
174	Learning Fine-grained Image Similarity with Deep Ranking	Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, Ying Wu	This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images.
175	Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns	Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki	In this paper, we define the soft attributed pattern (SAP) to describe the common subgraph pattern among a set of attributed relational graphs (ARGs), considering both the graphical structure and graph attributes.
176	Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters	Vladyslav Sydorov, Mayu Sakurada, Christoph H. Lampert	In this work, we emphasize the similarities between both architectures rather than their differences and we argue that such a unified view allows us to transfer ideas from one domain to the other.
177	Transfer Joint Matching for Unsupervised Domain Adaptation	Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu	In this paper, we show that both strategies are important and inevitable when the domain difference is substantially large.
178	Recognizing RGB Images by Learning from RGB-D Data	Lin Chen, Wen Li, Dong Xu	In this work, we propose a new framework for recognizing RGB images captured by the conventional cameras by leveraging a set of labeled RGB-D data, in which the depth features can be additionally extracted from the depth images.
179	Instance-weighted Transfer Learning of Active Appearance Models	Daniel Haase, Erik Rodner, Joachim Denzler	Therefore, we present a transfer learning method that is able to learn from related training data using an instance-weighted transfer technique.
180	Scalable Multitask Representation Learning for Scene Classification	Maksim Lapin, Bernt Schiele, Matthias Hein	In this paper, we propose a novel multitask learning method that learns a low-dimensional representation jointly with the corresponding classifiers, which are then able to profit from the latent inter-class correlations.
181	Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective	Novi Patricia, Barbara Caputo	We propose a learning to learn framework able to leverage over source data regardless of the origin of the distribution mismatch.
182	Constructing Robust Affinity Graphs for Spectral Clustering	Xiatian Zhu, Chen Change Loy, Shaogang Gong	In contrast to most existing clustering methods that typically employ all available features to construct affinity matrices with the Euclidean distance, which is often not an accurate representation of the underlying data structures, we propose a novel unsupervised approach to generating more robust affinity graphs via identifying and exploiting discriminative features for improving spectral clustering.
183	A Fast and Robust Algorithm to Count Topologically Persistent Holes in Noisy Clouds	Vitaliy Kurlin	We study the problem of counting holes in noisy clouds in the plane.
184	Co-localization in Real-World Images	Kevin Tang, Armand Joulin, Li-Jia Li, Li Fei-Fei	In this paper, we tackle the problem of co-localization in real-world images.
185	Spectral Clustering with Jensen-type Kernels and their Multi-point Extensions	Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan	Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multi-point’ kernels, and study their applications.
186	Fast and Robust Archetypal Analysis for Representation Learning	Yuansi Chen, Julien Mairal, Zaid Harchaoui	Our goal is to bring back into favour archetypal analysis.
187	Photometric Bundle Adjustment for Dense Multi-View 3D Modeling	Amael Delaunoy, Marc Pollefeys	Motivated by a Bayesian vision of the 3D multi-view reconstruction from images problem, we propose a dense 3D reconstruction technique that jointly refines the shape and the camera parameters of a scene by minimizing the photometric reprojection error between a generated model and the observed images, hence considering all pixels in the original images.
188	The Photometry of Intrinsic Images	Marc Serra, Olivier Penacchio, Robert Benavente, Maria Vanrell, Dimitris Samaras	This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem.
189	High Resolution 3D Shape Texture from Multiple Videos	Vagia Tsiminaki, Jean-Sebastien Franco, Edmond Boyer	We introduce a unified framework to leverage both possibilities for the estimation of an object’s high resolution texture.
190	PatchMatch Based Joint View Selection and Depthmap Estimation	Enliang Zheng, Enrique Dunn, Vladimir Jojic, Jan-Michael Frahm	We propose a multi-view depthmap estimation approach aimed at adaptively ascertaining the pixel level data associations between a reference image and all the elements of a source image set. We pose the problem within a probabilistic framework that jointly models pixel-level view selection and depthmap estimation given the local pairwise image photoconsistency.
191	Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras	Can Chen, Haiting Lin, Zhan Yu, Sing Bing Kang, Jingyi Yu	In this paper, we introduce a bilateral consistency metric on the surface camera (SCam) for light field stereo matching to handle significant occlusions.
192	Recovering Surface Details under General Unknown Illumination Using Shading and Coarse Multi-view Stereo	Di Xu, Qi Duan, Jianming Zheng, Juyong Zhang, Jianfei Cai, Tat-Jen Cham	This paper presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS).
193	Probabilistic Labeling Cost for High-Accuracy Multi-View Reconstruction	Ilya Kostrikov, Esther Horbert, Bastian Leibe	In this paper, we propose a novel labeling cost for multi- view reconstruction.
194	Complex Non-Rigid Motion 3D Reconstruction by Union of Subspaces	Yingying Zhu, Dong Huang, Fernando De La Torre, Simon Lucey	In this paper we make two contributions.
195	A Procrustean Markov Process for Non-Rigid Structure Recovery	Minsik Lee, Chong-Ho Choi, Songhwai Oh	In this paper, we propose a new probabilistic model that incorporates the smoothness constraint without requiring any prior knowledge.
196	Good Vibrations: A Modal Analysis Approach for Sequential Non-Rigid Structure from Motion	Antonio Agudo, Lourdes Agapito, Begona Calvo, Jose M. M. Montiel	We propose an online solution to non-rigid structure from motion that performs camera pose and 3D shape estimation of highly deformable surfaces on a frame-by-frame basis.
197	Robust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving	Shiyu Song, Manmohan Chandraker	This paper presents a real-time monocular SFM system that corrects for scale drift using a novel cue combination framework for ground plane estimation, yielding accuracy comparable to stereo over long driving sequences.
198	On the Quotient Representation for the Essential Manifold	Roberto Tron, Kostas Daniilidis	Previous works have proposed different characterizations of the space of essential matrices as a Riemannian manifold.
199	Efficient High-Resolution Stereo Matching using Local Plane Sweeps	Sudipta N. Sinha, Daniel Scharstein, Richard Szeliski	We present a stereo algorithm designed for speed and efficiency that uses local slanted plane sweeps to propose disparity hypotheses for a semi-global matching algorithm.
200	Cross-Scale Cost Aggregation for Stereo Matching	Kang Zhang, Yuqiang Fang, Dongbo Min, Lifeng Sun, Shiqiang Yang, Shuicheng Yan, Qi Tian	In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation.
201	Asymmetrical Gauss Mixture Models for Point Sets Matching	Wenbing Tao, Kun Sun	In this paper we propose an Asymmetrical GMM (AGMM) for point sets matching between a pair of images.
202	Fast and Reliable Two-View Translation Estimation	Johan Fredriksson, Olof Enqvist, Fredrik Kahl	In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera.
203	Graph Cut based Continuous Stereo Matching using Locally Shared Labels	Tatsunori Taniai, Yasuyuki Matsushita, Takeshi Naemura	We present an accurate and efficient stereo matching method using locally shared labels, a new labeling scheme that enables spatial propagation in MRF inference using graph cuts.
204	Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching	Aristotle Spyropoulos, Nikos Komodakis, Philippos Mordohai	We present a supervised learning approach for predicting the correctness of stereo matches based on a random forest and a set of features that capture various forms of information about each pixel.
205	Decorrelating Semantic Visual Attributes by Resisting the Urge to Share	Dinesh Jayaraman, Fei Sha, Kristen Grauman	We propose to resolve such confusions by jointly learning decorrelated, discriminative attribute models.
206	PANDA: Pose Aligned Networks for Deep Attribute Modeling	Ning Zhang, Manohar Paluri, Marc’Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev	We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion.
207	Learning Scalable Discriminative Dictionary with Sample Relatedness	Jiashi Feng, Stefanie Jegelka, Shuicheng Yan, Trevor Darrell	In this work, we propose a dictionary learning framework that flexibly adapts to the complexity of the given data set and reliably discovers the inherent discriminative middle-level binary features in the data.
208	DeepPose: Human Pose Estimation via Deep Neural Networks	Alexander Toshev, Christian Szegedy	We propose a method for human pose estimation based on Deep Neural Networks (DNNs).
209	Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation	Catalin Ionescu, Joao Carreira, Cristian Sminchisescu	This paper provides evidence for a positive answer, by leveraging (a) 2D human body part labeling in images, (b) second-order label-sensitive pooling over dynamically computed regions resulting from a hierarchical decomposition of the body, and (c) iterative structured-output modeling to contextualize the process based on 3D pose estimates.
210	3D Pictorial Structures for Multiple Human Pose Estimation	Vasileios Belagiannis, Sikandar Amin, Mykhaylo Andriluka, Bernt Schiele, Nassir Navab, Slobodan Ilic	In this work, we address the problem of 3D pose estimation of multiple humans from multiple views.
211	Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification	Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen	In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points.
212	Face Alignment at 3000 FPS via Regressing Local Binary Features	Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun	This paper presents a highly efficient, very accurate regression approach for face alignment.
213	A Compact and Discriminative Face Track Descriptor	Omkar M. Parkhi, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman	Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and classification.
214	DeepFace: Closing the Gap to Human-Level Performance in Face Verification	Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf	We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network.
215	Filter Forests for Learning Data-Dependent Convolutional Kernels	Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, Tim Paek	We propose ‘filter forests’ (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.
216	Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks	Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic	In this work we show how image representations learned with CNNs on large-scale annotated datasets can be efficiently transferred to other visual recognition tasks with limited amount of training data.
217	Large-scale Video Classification with Convolutional Neural Networks	Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei	We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training.
218	Convolutional Neural Networks for No-Reference Image Quality Assessment	Le Kang, Peng Ye, Yi Li, David Doermann	In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image.
219	Nonparametric Context Modeling of Local Appearance for Pose- and Expression-Robust Facial Landmark Localization	Brandon M. Smith, Jonathan Brandt, Zhe Lin, Li Zhang	We propose a data-driven approach to facial landmark localization that models the correlations between each landmark and its surrounding appearance features.
220	Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression Recognition	Mengyi Liu, Shiguang Shan, Ruiping Wang, Xilin Chen	In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. expressionlet.
221	Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders	Afshin Dehghan, Enrique G. Ortiz, Ruben Villegas, Mubarak Shah	In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question “Who do I look like?”
222	Unified Face Analysis by Iterative Multi-Output Random Forests	Xiaowei Zhao, Tae-Kyun Kim, Wenhan Luo	In this paper, we present a unified method for joint face image analysis, i.e., simultaneously estimating head pose, facial expression and landmark positions in real-world face images.
223	Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras	Kenneth Alberto Funes Mora, Jean-Marc Odobez	We propose a head pose invariant gaze estimation model for distant RGB-D cameras.
224	A Hierarchical Probabilistic Model for Facial Feature Detection	Yue Wu, Ziheng Wang, Qiang Ji	In this paper, we propose a hierarchical probabilistic model that could infer the true locations of facial features given the image measurements even if the face is with significant facial expression and pose.
225	RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models	Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic	In this paper, a novel method for the automatic construction of PSMs is proposed.
226	Non-Parametric Bayesian Constrained Local Models	Pedro Martins, Rui Caseiro, Jorge Batista	This work presents a novel non-parametric Bayesian formulation for aligning faces in unseen images.
227	Facial Expression Recognition via a Boosted Deep Belief Network	Ping Liu, Shizhong Han, Zibo Meng, Yan Tong	This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework.
228	Automatic Construction of Deformable Models In-The-Wild	Epameinondas Antonakos, Stefanos Zafeiriou	We propose the first – to the best of our knowledge – method for automatic construction of deformable models using images captured in totally unconstrained conditions, recently referred to as “in-the-wild”.
229	Learning-by-Synthesis for Appearance-based 3D Gaze Estimation	Yusuke Sugano, Yasuyuki Matsushita, Yoichi Sato	This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent.
230	Towards Multi-view and Partially-Occluded Face Alignment	Junliang Xing, Zhiheng Niu, Junshi Huang, Weiming Hu, Shuicheng Yan	We present a robust model to locate facial landmarks under different views and possibly severe occlusions.
231	Head Pose Estimation Based on Multivariate Label Distribution	Xin Geng, Yu Xia	Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms.
232	Efficient Boosted Exemplar-based Face Detection	Haoxiang Li, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Gang Hua	Notably, we propose to include non-face images as negative exemplars to actively suppress false detections to further improve the detection accuracy.
233	Gauss-Newton Deformable Part Models for Face Alignment in-the-Wild	Georgios Tzimiropoulos, Maja Pantic	To address this limitation, in this paper, we propose to jointly optimize a part-based, trained in-the-wild, flexible appearance model along with a global shape model which results in a joint translational motion model for the model parts via Gauss-Newton (GN) optimization.
234	Incremental Face Alignment in the Wild	Akshay Asthana, Stefanos Zafeiriou, Shiyang Cheng, Maja Pantic	We propose very efficient strategies to update the model and we show that is possible to automatically construct robust discriminative person and imaging condition specific models ‘in-the-wild’ that outperform state-of-the-art generic face alignment strategies.
235	One Millisecond Face Alignment with an Ensemble of Regression Trees	Vahid Kazemi, Josephine Sullivan	We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data.
236	Discriminative Deep Metric Learning for Face Verification in the Wild	Junlin Hu, Jiwen Lu, Yap-Peng Tan	This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild.
237	Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses	Meina Kan, Shiguang Shan, Hong Chang, Xilin Chen	Inspired by the observation that pose variations change non-linearly but smoothly, we propose to learn pose-robust features by modeling the complex non-linear transform from the non-frontal face images to frontal ones through a deep network in a progressive way, termed as stacked progressive auto-encoders (SPAE).
238	Deep Learning Face Representation from Predicting 10,000 Classes	Yi Sun, Xiaogang Wang, Xiaoou Tang	This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification.
239	3D-aided Face Recognition Robust to Expression and Pose Variations	Baptiste Chu, Sami Romdhani, Liming Chen	In this paper, we aim to endow state of the art face recognition SDKs with robustness to facial expression variations and pose changes by using an extended 3D Morphable Model (3DMM) which isolates identity variations from those due to facial expressions.
240	Learning Non-Linear Reconstruction Models for Image Set Classification	Munawar Hayat, Mohammed Bennamoun, Senjian An	We propose a deep learning framework for image set classification with application to face recognition.
241	Gesture Recognition Portfolios for Personalization	Angela Yao, Luc Van Gool, Pushmeet Kohli	In this paper, we address the problem of personalization in the context of gesture recognition, and propose a novel and extremely efficient way of doing personalization.
242	Sign Spotting using Hierarchical Sequential Patterns with Temporal Intervals	Eng-Jon Ong, Oscar Koller, Nicolas Pugeault, Richard Bowden	To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patterns that contain temporal intervals called {\em Sequential Interval Patterns} (SIP).
243	Automatic Feature Learning for Robust Shadow Detection	Salman Hameed Khan, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri	We present a practical framework to automatically detect shadows in real world scenes from a single photograph.
244	Packing and Padding: Coupled Multi-index for Accurate Image Retrieval	Liang Zheng, Shengjin Wang, Ziqiong Liu, Qi Tian	To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level.
245	Adaptive Object Retrieval with Kernel Reconstructive Hashing	Haichuan Yang, Xiao Bai, Jun Zhou, Peng Ren, Zhihong Zhang, Jian Cheng	Adaptive Object Retrieval with Kernel Reconstructive Hashing
246	Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval	Liang Zheng, Shengjin Wang, Wengang Zhou, Qi Tian	In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the intersection set.
247	Fast Supervised Hashing with Decision Trees for High-Dimensional Data	Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suter	Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data.
248	Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts	Xianjie Chen, Roozbeh Mottaghi, Xiaobai Liu, Sanja Fidler, Raquel Urtasun, Alan Yuille	We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution).
249	Associative Embeddings for Large-scale Knowledge Transfer with Self-assessment	Alexander Vezhnevets, Vittorio Ferrari	We propose a method for knowledge transfer between semantically related classes in ImageNet.
250	Detecting Objects using Deformation Dictionaries	Bharath Hariharan, C. L. Zitnick, Piotr Dollar	In this paper, we show the counter-intuitive result that it is possible to achieve similar accuracy using a small dictionary of deformations.
251	Persistence-based Structural Recognition	Chunyuan Li, Maks Ovsjanikov, Frederic Chazal	This paper presents a framework for object recognition using topological persistence.
252	Inferring Unseen Views of People	Chao-Yeh Chen, Kristen Grauman	We pose unseen view synthesis as a probabilistic tensor completion problem.
253	Birdsnap: Large-scale Fine-grained Visual Categorization of Birds	Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L. Alexander, David W. Jacobs, Peter N. Belhumeur	We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species.
254	Predicting Object Dynamics in Scenes	David F. Fouhey, C. L. Zitnick	In this paper, we investigate learning this commonsense knowledge from data.
255	Enriching Visual Knowledge Bases via Object Discovery and Segmentation	Xinlei Chen, Abhinav Shrivastava, Abhinav Gupta	In this paper, we propose to enrich these knowledge bases by automatically discovering objects and their segmentations from noisy Internet images.
256	Seeing the Arrow of Time	Lyndsey C. Pickup, Zheng Pan, Donglai Wei, YiChang Shih, Changshui Zhang, Andrew Zisserman, Bernhard Scholkopf, William T. Freeman	We explore three methods by which we might detect Time’s Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time.
257	Hierarchical Feature Hashing for Fast Dimensionality Reduction	Bin Zhao, Eric P. Xing	In this paper, we propose hierarchical feature hashing to effectively reduce dimensionality of parameter space without sacrificing classification accuracy, and at the same time exploit information in semantic taxonomy among categories.
258	Modeling Image Patches with a Generic Dictionary of Mini-Epitomes	George Papandreou, Liang-Chieh Chen, Alan L. Yuille	The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks.
259	Simplex-Based 3D Spatio-Temporal Feature Description for Action Recognition	Hao Zhang, Wenjun Zhou, Christopher Reardon, Lynne E. Parker	We present a novel feature description algorithm to describe 3D local spatio-temporal features for human action recognition.
260	In Search of Inliers: 3D Correspondence by Local and Global Voting	Anders Glent Buch, Yang Yang, Norbert Kruger, Henrik Gordon Petersen	We present a method for finding correspondence between 3D models.
261	Collective Matrix Factorization Hashing for Multimodal Data	Guiguang Ding, Yuchen Guo, Jile Zhou	In this paper, we study the problems of learning hash functions in the context of multimodal data for cross-view similarity search.
262	Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers	Minsu Cho, Jian Sun, Olivier Duchenne, Jean Ponce	In this paper, we propose a max-pooling approach to graph matching, which is not only resilient to deformations but also remarkably tolerant to outliers.
263	Locality in Generic Instance Search from One Example	Ran Tao, Efstratios Gavves, Cees G.M. Snoek, Arnold W.M. Smeulders	This paper aims for generic instance search from a single example.
264	Congruency-Based Reranking	Itai Ben-Shalom, Noga Levy, Lior Wolf, Nachum Dershowitz, Adiel Ben-Shalom, Roni Shweka, Yaacov Choueka, Tamir Hazan, Yaniv Bar	We present a tool for re-ranking the results of a specific query by considering the (n+1) × (n+1) matrix of pairwise similarities among the elements of the set of n retrieved results and the query itself.
265	Asymmetric Sparse Kernel Approximations for Large-scale Visual Search	Damek Davis, Jonathan Balzer, Stefano Soatto	We introduce an asymmetric sparse approximate embedding optimized for fast kernel comparison operations arising in large-scale visual search.
266	Locally Linear Hashing for Extracting Non-Linear Manifolds	Go Irie, Zhenguo Li, Xiao-Ming Wu, Shih-Fu Chang	In this paper, we tackle this problem by reconstructing the locally linear structures of manifolds in the binary Hamming space, which can be learned by locality-sensitive sparse coding.
267	Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation	Vasiliy Karasev, Avinash Ravichandran, Stefano Soatto	We describe an information-driven active selection approach to determine which detectors to deploy at which location in which frame of a video to minimize semantic class label uncertainty at every pixel, with the smallest computational cost that ensures a given uncertainty bound.
268	Distance Encoded Product Quantization	Jae-Pil Heo, Zhe Lin, Sung-Eui Yoon	In this paper, we explore a simple question: is it best to use all the bit budget for encoding a cluster index in each subspace?
269	Collaborative Hashing	Xianglong Liu, Junfeng He, Cheng Deng, Bo Lang	To fully explore the duality between the two views, we propose a collaborative hashing scheme for the data in matrix form to enable fast search in various applications such as image search using bag of words and recommendation using user-item ratings.
270	Scalable Object Detection using Deep Neural Networks	Dumitru Erhan, Christian Szegedy, Alexander Toshev, Dragomir Anguelov	In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest.
271	Multiview Shape and Reflectance from Natural Illumination	Geoffrey Oxholm, Ko Nishino	To this end, we derive a probabilistic geometry estimation method that fully exploits the rich signal embedded in complex appearance.
272	Reflectance and Fluorescent Spectra Recovery based on Fluorescent Chromaticity Invariance under Varying Illumination	Ying Fu, Antony Lam, Yasuyuki Kobashi, Imari Sato, Takahiro Okabe, Yoichi Sato	In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants.
273	What Camera Motion Reveals About Shape With Unknown BRDF	Manmohan Chandraker	This paper addresses the remaining problem of determining shape from the (small or differential) motion of the camera, for unknown isotropic BRDFs.
274	Photometric Stereo using Constrained Bivariate Regression for General Isotropic Surfaces	Satoshi Ikehata, Kiyoharu Aizawa	This paper presents a photometric stereo method that is purely pixelwise and handles general isotropic surfaces in a stable manner. Following the recently proposed sum-of-lobes representation of the isotropic reflectance function, we constructed a constrained bivariate regression problem where the regression function is approximated by smooth, bivariate Bernstein polynomials.
275	Robust Separation of Reflection from Multiple Images	Xiaojie Guo, Xiaochun Cao, Yi Ma	In this paper, we propose a robust method to separate these two layers from multiple images, which exploits the correlation of the transmitted layer across multiple images, and the sparsity and independence of the gradient fields of the two layers.
276	Surface-from-Gradients: An Approach Based on Discrete Geometry Processing	Wuyuan Xie, Yunbo Zhang, Charlie C. L. Wang, Ronald C.-K. Chung	In this paper, we propose an efficient method to reconstruct surface-from-gradients (SfG).
277	Socially-aware Large-scale Crowd Forecasting	Alexandre Alahi, Vignesh Ramanathan, Li Fei-Fei	We propose to quantitatively study crowded environments by introducing a dataset of 42 million trajectories collected in train stations.
278	L0 Regularized Stationary Time Estimation for Crowd Group Analysis	Shuai Yi, Xiaogang Wang, Cewu Lu, Jiaya Jia	We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance. We provide the first public benchmark dataset for stationary time estimation and stationary group analysis.
279	Scene-Independent Group Profiling in Crowd	Jing Shao, Chen Change Loy, Xiaogang Wang	In this study we show that fundamental group-level properties, such as intra-group stability and inter-group conflict, can be systematically quantified by visual descriptors.
280	Temporal Sequence Modeling for Video Event Detection	Yu Cheng, Quanfu Fan, Sharath Pankanti, Alok Choudhary	We present a novel approach for event detection in video by temporal sequence modeling.
281	Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts	Subhabrata Bhattacharya, Mahdi M. Kalayeh, Rahul Sukthankar, Mubarak Shah	This paper proposes a novel representation that captures the temporal dynamics of windowed mid-level concept detectors in order to improve complex event recognition.
282	Video Event Detection by Inferring Temporal Instance Labels	Kuan-Ting Lai, Felix X. Yu, Ming-Syan Chen, Shih-Fu Chang	In this work, we propose a novel instance-based video event detection approach.
283	Backscatter Compensated Photometric Stereo with 3 Sources	Chourmouzios Tsiotsios, Maria E. Angelopoulou, Tae-Kyun Kim, Andrew J. Davison	We compare our method with previous approaches through extensive experimental results, where a variety of objects are imaged in a big water tank whose turbidity is systematically increased, and show reconstruction quality which degrades little relative to clean water results even with a very significant scattering level.
284	Calibrating a Non-isotropic Near Point Light Source using a Plane	Jaesik Park, Sudipta N. Sinha, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon	We have evaluated our method on synthetic data quantitavely.
285	A New Perspective on Material Classification and Ink Identification	Rakesh Shiradkar, Li Shen, George Landon, Sim Heng Ong, Ping Tan	We apply these results on ink classification, which can be used in forensics and analyzing historical manuscripts.
286	High Quality Photometric Reconstruction using a Depth Camera	Sk. Mohammadul Haque, Avishek Chatterjee, Venu Madhav Govindu	In this paper we present a depth-guided photometric 3D reconstruction method that works solely with a depth camera like the Kinect.
287	Robust Surface Reconstruction via Triple Sparsity	Hicham Badri, Hussein Yahia, Driss Aboutajdine	We present in this paper a new optimization method for robust surface reconstruction.
288	Scattering Parameters and Surface Normals from Homogeneous Translucent Materials using Photometric Stereo	Bo Dong, Kathleen D. Moore, Weiyi Zhang, Pieter Peers	This paper proposes a novel photometric stereo solution to jointly estimate surface normals and scattering parameters from a globally planar, homogeneous, translucent object.
289	Better Shading for Better Shape Recovery	Moumen T. El-Melegy, Aly S. Abdelrahim, Aly A. Farag	This paper presents a simple shading-correction algorithm that transforms the image to a new image that better satisfies the assumptions typically needed by existing algorithms, thus improving the accuracy of shape recovery.
290	Stable and Informative Spectral Signatures for Graph Matching	Nan Hu, Raif M. Rustamov, Leonidas Guibas	In this paper, we consider the approximate weighted graph matching problem and introduce stable and informative first and second order compatibility terms suitable for inclusion into the popular integer quadratic program formulation.
291	Deformable Object Matching via Deformation Decomposition based 2D Label MRF	Kangwei Liu, Junge Zhang, Kaiqi Huang, Tieniu Tan	Motivated by this, we analyze the deformation physically and propose a novel deformation decomposition model to represent various deformations. To provide a quantitative benchmark, we build a deformation matching database with an evaluation criterion.
292	Locally Optimized Product Quantization for Approximate Nearest Neighbor Search	Yannis Kalantidis, Yannis Avrithis	We present a simple vector quantizer that combines low distortion with fast search and apply it to approximate nearest neighbor (ANN) search in high dimensional spaces.
293	Multi-source Deep Learning for Human Pose Estimation	Wanli Ouyang, Xiao Chu, Xiaogang Wang	This paper proposes to build a multi-source deep model in order to extract non-linear representation from these different aspects of information sources.
294	Posebits for Monocular Human Pose Estimation	Gerard Pons-Moll, David J. Fleet, Bodo Rosenhahn	We introduce posebits, a posebit database, a method for selecting useful posebits for pose estimation and a structural SVM model for posebit inference.
295	Real-time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera	Mao Ye, Ruigang Yang	In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals.
296	Mixing Body-Part Sequences for Human Pose Estimation	Anoop Cherian, Julien Mairal, Karteek Alahari, Cordelia Schmid	In this paper, we present a method for estimating articulated human poses in videos.
297	Robust Estimation of 3D Human Poses from a Single Image	Chunyu Wang, Yizhou Wang, Zhouchen Lin, Alan L. Yuille, Wen Gao	We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector.
298	Fisher and VLAD with FLAIR	Koen E. A. van de Sande, Cees G. M. Snoek, Arnold W. M. Smeulders	Where a simplification in the representation is tempting, we exploit novel representations while maintaining accuracy.
299	Immediate, Scalable Object Category Detection	Yusuf Aytar, Andrew Zisserman	The objective of this work is object category detection in large scale image datasets in the manner of Video Google an object category is specified by a HOG classifier template, and retrieval is immediate at run time.
300	Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model	Golnaz Ghiasi, Charless C. Fowlkes	In this paper we describe a hierarchical deformable part model for face detection and keypoint localization that explicitly models occlusions of parts.
301	Word Channel Based Multiscale Pedestrian Detection Without Image Resizing and Using Only One Classifier	Arthur Daniel Costea, Sergiu Nedevschi	In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale.
302	Parsing Occluded People	Golnaz Ghiasi, Yi Yang, Deva Ramanan, Charless C. Fowlkes	We take a strongly supervised, non-parametric approach to modeling occlusion by learning deformable models with many local part mixture templates using large quantities of synthetically generated training data.
303	Multi-fold MIL Training for Weakly Supervised Object Localization	Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid	Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations.
304	Generating Object Segmentation Proposals using Global and Local Search	Pekka Rantalankila, Juho Kannala, Esa Rahtu	We present a method for generating object segmentation proposals from groups of superpixels.
305	A Novel Chamfer Template Matching Method Using Variational Mean Field	Duc Thanh Nguyen	This paper proposes a novel mean field-based Chamfer template matching method.
306	Confidence-Rated Multiple Instance Boosting for Object Detection	Karim Ali, Kate Saenko	In this paper, we propose a new MIL method for object detection that is capable of handling the noisier automatically obtained annotations.
307	COSTA: Co-Occurrence Statistics for Zero-Shot Classification	Thomas Mensink, Efstratios Gavves, Cees G.M. Snoek	In this paper we aim for zero-shot classification, that is visual recognition of an unseen class by using knowledge transfer from known classes.
308	Analysis by Synthesis: 3D Object Recognition by Object Reconstruction	Mohsen Hejrati, Deva Ramanan	We introduce a new approach for recognizing and reconstructing 3D objects in images.
309	Submodular Object Recognition	Fan Zhu, Zhuolin Jiang, Ling Shao	We present a novel object recognition framework based on multiple figure-ground hypotheses with a large object spatial support, generated by bottom-up processes and mid-level cues in an unsupervised manner.
310	Multimodal Learning in Loosely-organized Web Images	Kun Duan, David J. Crandall, Dhruv Batra	In this paper, we propose a framework to model these “loosely organized” multimodal datasets, and show how to perform loosely-supervised learning using a novel latent Conditional Random Field framework.
311	Generalized Max Pooling	Naila Murray, Florent Perronnin	We propose a novel pooling mechanism that achieves the same effect as max-pooling but is applicable beyond the BOV and especially to the state-of-the-art Fisher Vector– hence the name Generalized Max Pooling (GMP).
312	Domain Adaptation on the Statistical Manifold	Mahsa Baktashmotlagh, Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann	In this paper, we tackle the problem of unsupervised domain adaptation for classification.
313	Nonparametric Part Transfer for Fine-grained Recognition	Christoph Goring, Erik Rodner, Alexander Freytag, Joachim Denzler	In the following paper, we present an approach for fine-grained recognition based on a new part detection method.
314	The Fastest Deformable Part Model for Object Detection	Junjie Yan, Zhen Lei, Longyin Wen, Stan Z. Li	This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets.
315	Unsupervised Learning of Dictionaries of Hierarchical Compositional Models	Jifeng Dai, Yi Hong, Wenze Hu, Song-Chun Zhu, Ying Nian Wu	This paper proposes an unsupervised method for learning dictionaries of hierarchical compositional models for representing natural images.
316	Quasi Real-Time Summarization for Consumer Videos	Bin Zhao, Eric P. Xing	In this work, we propose online video highlighting, a principled way of generating short video summarizing the most important and interesting contents of an unedited and unstructured video, costly both time-wise and financially for manual processing.
317	Gait Recognition under Speed Transition	Al Mansur, Yasushi Makihara, Rasyid Aqmar, Yasushi Yagi	This paper describes a method of gait recognition from image sequences wherein a subject is accelerating or decelerating.
318	Video Classification using Semantic Concept Co-occurrences	Shayan Modiri Assari, Amir Roshan Zamir, Mubarak Shah	In this paper, we propose a contextual approach to video classification based on Generalized Maximum Clique Problem (GMCP) which uses the co-occurrence of concepts as the context model.
319	Temporal Segmentation of Egocentric Videos	Yair Poleg, Chetan Arora, Shmuel Peleg	In this paper we address the motion cues for video segmentation.
320	Efficient Action Localization with Approximately Normalized Fisher Vectors	Dan Oneata, Jakob Verbeek, Cordelia Schmid	In this paper we present approximations to both these normalizations, which yield significant improvements in the memory and computational costs of the FV when used for localization.
321	Unsupervised Trajectory Modelling using Temporal Information via Minimal Paths	Brais Cancela, Alberto Iglesias, Marcos Ortega, Manuel G. Penedo	This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based in the hypothesis that, when people try to reach a destination, they use the path that takes less time, taking into account environmental information like the type of terrain or what other people did before.
322	A Hierarchical Context Model for Event Recognition in Surveillance Video	Xiaoyang Wang, Qiang Ji	Unlike existing researches that generally integrate the context information at one of the three levels, we propose a hierarchical context model that simultaneously exploits contexts at all three levels and systematically incorporate them into event recognition.
323	DISCOVER: Discovering Important Segments for Classification of Video Events and Recounting	Chen Sun, Ram Nevatia	We introduce an evidence localization model where evidence locations are modeled as latent variables.
324	Towards Good Practices for Action Video Encoding	Jianxin Wu, Yu Zhang, Weiyao Lin	Combining this new perspective with observed VLAD data distribution properties, we propose a simple, lightweight, but powerful bimodal encoding method.
325	Improving Semantic Concept Detection through the Dictionary of Visually-distinct Elements	Afshin Dehghan, Haroon Idrees, Mubarak Shah	In this paper, we present an approach that leverages the strengths of semantic concepts and the machine-discovered DOVE by learning a relationship between them.
326	Efficient Feature Extraction, Encoding and Classification for Action Recognition	Vadim Kantorov, Ivan Laptev	Our method improves the speed of video feature extraction, feature encoding and action classification by two orders of magnitude at the cost of minor reduction in recognition accuracy.
327	3D Pose from Motion for Cross-view Action Recognition via Non-linear Circulant Temporal Encoding	Ankur Gupta, Julieta Martinez, James J. Little, Robert J. Woodham	We describe a new approach to transfer knowledge across views for action recognition by using examples from a large collection of unlabelled mocap data.
328	Human Action Recognition Based on Context-Dependent Graph Kernels	Baoxin Wu, Chunfeng Yuan, Weiming Hu	In this paper, we construct a two-graph model to represent human actions by recording the spatial and temporal relationships among local features.
329	Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras	Yen-Yu Lin, Ju-Hsuan Hua, Nick C. Tang, Min-Hung Chen, Hong-Yuan Mark Liao	We propose an alternative scenario to address this problem, and illustrate it with the application to action recognition.
330	DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition	Lin Sun, Kui Jia, Tsung-Han Chan, Yuqiang Fang, Gang Wang, Shuicheng Yan	In this paper, we propose to combine SFA with deep learning techniques to learn hierarchical representations from the video data itself.
331	A Cause and Effect Analysis of Motion Trajectories for Modeling Actions	Sanath Narayan, Kalpathi R. Ramakrishnan	In this paper we propose a causality based approach for quantifying the interactions to aid action classification.
332	From Stochastic Grammar to Bayes Network: Probabilistic Parsing of Complex Activity	Nam N. Vo, Aaron F. Bobick	We propose a probabilistic method for parsing a temporal sequence such as a complex activity defined as composition of sub-activities/actions.
333	Cross-view Action Modeling, Learning and Recognition	Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu, Song-Chun Zhu	This paper proposes effective methods to learn the structure and parameters of MST-AOG.
334	Visual Semantic Search: Retrieving Videos via Complex Textual Queries	Dahua Lin, Sanja Fidler, Chen Kong, Raquel Urtasun	In this paper, we tackle the problem of retrieving videos using complex natural language queries.
335	Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised Concepts	Shuang Wu, Sravanthi Bondugula, Florian Luisier, Xiaodan Zhuang, Pradeep Natarajan	In this paper, we present a general framework for the zeroshot learning problem of performing high-level event detection with no training exemplars, using only textual descriptions.
336	Dual Linear Regression Based Classification for Face Cluster Recognition	Liang Chen	We are dealing with the face cluster recognition problem where there are multiple images per subject in both gallery and probe sets.
337	Bags of Spacetime Energies for Dynamic Scene Recognition	Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes	This paper presents a unified bag of visual word (BoW) framework for dynamic scene recognition.
338	Feature-Independent Action Spotting Without Human Localization, Segmentation or Frame-wise Tracking	Chuan Sun, Marshall Tappen, Hassan Foroosh	In this paper, we propose an unsupervised framework for action spotting in videos that does not depend on any specific feature (e.g. HOG/HOF, STIP, silhouette, bag-of-words, etc.). We also created a challenging dataset called Heavily Perturbed Video Array (HPVA) to validate the robustness of our framework under heavily perturbed situations.
339	Multiscale Centerline Detection by Learning a Scale-Space Distance Transform	Amos Sironi, Vincent Lepetit, Pascal Fua	We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes.
340	Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images	Hyunwoo J. Kim, Nagesh Adluru, Maxwell D. Collins, Moo K. Chung, Barbara B. Bendlin, Sterling C. Johnson, Richard J. Davidson, Vikas Singh	We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression a manifold-valued dependent variable against multiple independent variables, i.e., f : Rn -> M.
341	Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction	Chen Chen, Junzhou Huang, Lei He, Hongsheng Li	In this paper, we propose a novel algorithm for structured sparsity reconstruction.
342	Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis	Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen	Regarding multi-modal data fusion, we propose a novel coupled boosting algorithm that analyzes the pairwise coupled-diversity correlation between modalities.
343	Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields	Demian Wassermann, James Ross, George Washko, William M. Wells III, Raul San Jose-Estepar	The main contribution of this work is a framework to register anatomical structures characterized as a point set where each point has an associated symmetric matrix.
344	Tracking Indistinguishable Translucent Objects over Time using Weakly Supervised Structured Learning	Luca Fiaschi, Ferran Diego, Konstantin Gregor, Martin Schiegg, Ullrich Koethe, Marta Zlatic, Fred A. Hamprecht	For this challenging problem, we propose a novel model which handles occlusions, complex motions and non-rigid deformations by jointly optimizing the flows of multiple latent intensities across frames.
345	Scale-space Processing Using Polynomial Representations	Gou Koutaki, Keiichi Uchimura	In this study, we propose the application of principal components analysis (PCA) to scale-spaces.
346	Single Image Layer Separation using Relative Smoothness	Yu Li, Michael S. Brown	We introduce a novel strategy that regularizes the gradients of the two layers such that one has a long tail distribution and the other a short tail distribution.
347	Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity	Chen Chen, Yeqing Li, Wei Liu, Junzhou Huang	In this paper, we propose a novel method for image fusion from a high resolution panchromatic image and a low resolution multispectral image at the same geographical location.
348	Segmentation-Free Dynamic Scene Deblurring	Tae Hyun Kim, Kyoung Mu Lee	In this paper, we study a motion segmentation-free dynamic scene deblurring method, which is unlike other conventional methods.
349	Shrinkage Fields for Effective Image Restoration	Uwe Schmidt, Stefan Roth	The goal of this paper is to develop an effective approach to image restoration that offers both computational efficiency and high restoration quality.
350	Camouflaging an Object from Many Viewpoints	Andrew Owens, Connelly Barnes, Alex Flint, Hanumant Singh, William Freeman	To do this, we introduce several background matching algorithms that attempt to make the object look like whatever is behind it.
351	Learning Optimal Seeds for Diffusion-based Salient Object Detection	Song Lu, Vijay Mahadevan, Nuno Vasconcelos	In this work, we propose a method to learn optimal seeds for object saliency.
352	Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images	Eleonora Vig, Michael Dorr, David Cox	We identify those instances of a richly-parameterized bio-inspired model family (hierarchical neuromorphic networks) that successfully predict image saliency.
353	Saliency Detection on Light Field	Nianyi Li, Jinwei Ye, Yu Ji, Haibin Ling, Jingyi Yu	We explore the problem of using light fields as input for saliency detection.
354	Saliency Optimization from Robust Background Detection	Wangjiang Zhu, Shuang Liang, Yichen Wei, Jian Sun	In this work, we present new methods to address these issues.
355	A Reverse Hierarchy Model for Predicting Eye Fixations	Tianlin Shi, Ming Liang, Xiaolin Hu	Inspired by the theory, we develop a computational model for saliency detection in images.
356	100+ Times Faster Weighted Median Filter (WMF)	Qi Zhang, Li Xu, Jiaya Jia	Our contribution is on a new joint-histogram representation, median tracking, and a new data structure that enables fast data access.
357	Edge-aware Gradient Domain Optimization Framework for Image Filtering by Local Propagation	Miao Hua, Xiaohui Bie, Minying Zhang, Wencheng Wang	In this paper, we present new constraints explicitly to better preserve edges for general gradient domain image filtering and theoretically analyse why these constraints are edge-aware.
358	Super-Resolving Noisy Images	Abhishek Singh, Fatih Porikli, Narendra Ahuja	In this paper, we show that high frequency content in the noisy image (which is ordinarily removed by denoising algorithms) can be effectively used to obtain the missing textural details in the HR domain.
359	Sparse Dictionary Learning for Edit Propagation of High-Resolution Images	Xiaowu Chen, Dongqing Zou, Jianwei Li, Xiaochun Cao, Qinping Zhao, Hao Zhang	We introduce a method of sparse dictionary learning for edit propagation of high-resolution images or video.
360	Weighted Nuclear Norm Minimization with Application to Image Denoising	Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng	In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights.
361	Using Projection Kurtosis Concentration Of Natural Images For Blind Noise Covariance Matrix Estimation	Xing Zhang, Siwei Lyu	We demonstrate the effectiveness of our blind noise covariance matrix estimation method on natural images.
362	Blind Image Quality Assessment using Semi-supervised Rectifier Networks	Huixuan Tang, Neel Joshi, Ashish Kapoor	We present a new blind image quality measure that addresses these difficulties by learning a robust, nonlinear kernel regression function using a rectifier neural network.
363	Separable Kernel for Image Deblurring	Lu Fang, Haifeng Liu, Feng Wu, Xiaoyan Sun, Houqiang Li	In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system.
364	Joint Depth Estimation and Camera Shake Removal from Single Blurry Image	Zhe Hu, Li Xu, Ming-Hsuan Yang	To this end, we present a unified layer-based model for depth-involved deblurring.
365	Deblurring Text Images via L0-Regularized Intensity and Gradient Prior	Jinshan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang	We propose a simple yet effective L_0-regularized prior based on intensity and gradient for text image deblurring.
366	Total Variation Blind Deconvolution: The Devil is in the Details	Daniele Perrone, Paolo Favaro	In this paper we study the problem of blind deconvolution.
367	Single Image Super-resolution using Deformable Patches	Yu Zhu, Yanning Zhang, Alan L. Yuille	We proposed a deformable patches based method for single image super-resolution.
368	Multi-Shot Imaging: Joint Alignment, Deblurring and Resolution-Enhancement	Haichao Zhang, Lawrence Carin	In this paper, we propose a blind multi-image restoration method which can achieve joint alignment, non-uniform deblurring, together with resolution enhancement from multiple low-quality images.
369	CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images	Huanjing Yue, Xiaoyan Sun, Jingyu Yang, Feng Wu	In this paper, we propose a novel two-step scheme to filter heavy noise from images with the assistance of retrieved Web images.
370	Multipoint Filtering with Local Polynomial Approximation and Range Guidance	Xiao Tan, Changming Sun, Tuan D. Pham	This paper presents a novel guided image filtering method using multipoint local polynomial approximation (LPA) with range guidance.
371	Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising	Yi Peng, Deyu Meng, Zongben Xu, Chenqiang Gao, Yi Yang, Biao Zhang	In this paper we propose an effective MSI denoising approach by combinatorially considering two intrinsic characteristics underlying an MSI: the nonlocal similarity over space and the global correlation across spectrum.
372	Robust 3D Features for Matching between Distorted Range Scans Captured by Moving Systems	Xiangqi Huang, Bo Zheng, Takeshi Masuda, Katsushi Ikeuchi	In this paper, we propose novel 3D features which can be robustly extracted and matched even for the distorted 3D surface captured by a moving system.
373	Discriminative Blur Detection Features	Jianping Shi, Li Xu, Jiaya Jia	To avail evaluation, we build a new blur perception dataset containing thousands of images with labeled ground-truth.
374	Detection, Rectification and Segmentation of Coplanar Repeated Patterns	James Pritts, Ondrej Chum, Jiri Matas	This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns.
375	Mirror Symmetry Histograms for Capturing Geometric Properties in Images	Marcelo Cicconet, Davi Geiger, Kristin C. Gunsalus, Michael Werman	We propose a data structure that captures global geometric properties in images: Histogram of Mirror Symmetry Coefficients.
376	A Learning-to-Rank Approach for Image Color Enhancement	Jianzhou Yan, Stephen Lin, Sing Bing Kang, Xiaoou Tang	We present a machine-learned ranking approach for automatically enhancing the color of a photograph.
377	Investigating Haze-relevant Features in A Learning Framework for Image Dehazing	Ketan Tang, Jianchao Yang, Jue Wang	In this paper, we systematically investigate different haze-relevant features in a learning framework to identify the best feature combination for image dehazing.
378	Quality Assessment for Comparing Image Enhancement Algorithms	Zhengying Chen, Tingting Jiang, Yonghong Tian	In this paper, we propose a framework to do quality assessment for comparing image enhancement algorithms. We construct a dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms, and then do subjective assessment in a pair-wise way to get the relative ranking of these enhanced images.
379	Shadow Removal from Single RGB-D Images	Yao Xiao, Efstratios Tsougenis, Chi-Keung Tang	We present the first automatic method to remove shadows from single RGB-D images.
380	Manifold Based Dynamic Texture Synthesis from Extremely Few Samples	Hongteng Xu, Hongyuan Zha, Mark A. Davenport	In this paper, we present a novel method to synthesize dynamic texture sequences from extremely few samples, e.g., merely two possibly disparate frames, leveraging both Markov Random Fields (MRFs) and manifold learning.
381	The Synthesizability of Texture Examples	Dengxin Dai, Hayko Riemenschneider, Luc Van Gool	We introduce a dataset (21,302 textures) of which all images have been annotated in terms of their synthesizability.
382	Reconstructing Evolving Tree Structures in Time Lapse Sequences	Przemyslaw Glowacki, Miguel Amavel Pinheiro, Engin Turetken, Raphael Sznitman, Daniel Lebrecht, Jan Kybic, Anthony Holtmaat, Pascal Fua	We propose an approach to reconstructing tree structures that evolve over time in 2D images and 3D image stacks such as neuronal axons or plant branches.
383	Total-Variation Minimization on Unstructured Volumetric Mesh: Biophysical Applications on Reconstruction of 3D Ischemic Myocardium	Jingjia Xu, Azar Rahimi Dehaghani, Fei Gao, Linwei Wang	This paper describes the development and application of a new approach to total-variation (TV) minimization for reconstruction problems on geometrically-complex and unstructured volumetric mesh.
384	Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI	Yuanxiang Wang, Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri	In this paper, we propose a new intrinsic recursive filter on the product manifold of shape and orientation.
385	Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation	Erkang Cheng, Yu Pang, Ying Zhu, Jingyi Yu, Haibin Ling	To address these issues, we propose a new deformable tracking method using the tensor-based algorithm with model propagation.
386	Patch-based Evaluation of Image Segmentation	Christian Ledig, Wenzhe Shi, Wenjia Bai, Daniel Rueckert	To address this problem, we introduce Patch-based Evaluation of Image Segmentation (PEIS), a general method to assess segmentation quality.
387	Evaluation of Scan-Line Optimization for 3D Medical Image Registration	Simon Hermann	This paper introduces this combination as a general and effective optimization technique.
388	Classification of Histology Sections via Multispectral Convolutional Sparse Coding	Yin Zhou, Hang Chang, Kenneth Barner, Paul Spellman, Bahram Parvin	We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC).
389	Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer’s Disease Diagnosis	Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen	In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix.
390	Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification	Luping Zhou, Lei Wang, Philip Ogunbona	In this paper, we propose a learning framework to effectively improve the discriminative power of SICEs by taking advantage of the samples in the opposite class.
391	A Bayesian Framework For the Local Configuration of Retinal Junctions	Touseef Ahmad Qureshi, Andrew Hunter, Bashir Al-Diri	This paper presents a Bayesian approach to resolving the configuration of vascular junctions to correctly construct the vascular trees.
392	Learning-Based Atlas Selection for Multiple-Atlas Segmentation	Gerard Sanroma, Guorong Wu, Yaozong Gao, Dinggang Shen	To solve this simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would eventually lead to more accurate image segmentation.
393	Fully Automated Non-rigid Segmentation with Distance Regularized Level Set Evolution Initialized and Constrained by Deep-structured Inference	Tuan Anh Ngo, Gustavo Carneiro	We propose a new fully automated non-rigid segmentation approach based on the distance regularized level set method that is initialized and constrained by the results of a structured inference using deep belief networks.
394	FAST LABEL: Easy and Efficient Solution of Joint Multi-Label and Estimation Problems	Ganesh Sundaramoorthi, Byung-Woo Hong	We give sample Matlab code for the multi-label Chan-Vese problem in this paper!
395	Learning to Group Objects	Victoria Yanulevskaya, Jasper Uijlings, Nicu Sebe	This paper presents a novel method to generate a hypothesis set of class-independent object regions.
396	Unsupervised Multi-Class Joint Image Segmentation	Fan Wang, Qixing Huang, Maks Ovsjanikov, Leonidas J. Guibas	In this paper, we present a novel method to jointly segment a set of images containing objects from multiple classes.
397	Semantic Object Selection	Ejaz Ahmed, Scott Cohen, Brian Price	In this paper, we present a system with a far simpler input method: the user needs only give the name of the desired object.
398	Discrete-Continuous Gradient Orientation Estimation for Faster Image Segmentation	Michael Donoser, Dieter Schmalstieg	In this paper, we demonstrate that based on a discrete-continuous optimization of oriented gradient signals, we are able to provide segmentation performance competitive to state-of-the-art on BSDS 500 (even without any spectral analysis) while reducing computation time by a factor of 40 and memory demands by a factor of 10.
399	Object-based Multiple Foreground Video Co-segmentation	Huazhu Fu, Dong Xu, Bao Zhang, Stephen Lin	We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set.
400	Parsing World’s Skylines using Shape-Constrained MRFs	Rashmi Tonge, Subhransu Maji, C. V. Jawahar	We propose an approach for segmenting the individual buildings in typical skyline images.
401	Clothing Co-Parsing by Joint Image Segmentation and Labeling	Wei Yang, Ping Luo, Liang Lin	We propose a data-driven framework consisting of two phases of inference. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system.
402	Tell Me What You See and I will Show You Where It Is	Jia Xu, Alexander G. Schwing, Raquel Urtasun	In this paper, we show that this problem can be formalized as an instance of learning in a latent structured prediction framework, where the graphical model encodes the presence and absence of a class as well as the assignments of semantic labels to superpixels.
403	Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision	Liang-Chieh Chen, Sanja Fidler, Alan L. Yuille, Raquel Urtasun	In this paper, we show how to exploit 3D information to automatically generate very accurate object segmentations given annotated 3D bounding boxes.
404	Efficient Structured Parsing of Facades Using Dynamic Programming	Andrea Cohen, Alexander G. Schwing, Marc Pollefeys	We propose a sequential optimization technique for segmenting a rectified image of a facade into semantic categories.
405	Dense Semantic Image Segmentation with Objects and Attributes	Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother, Philip H. S. Torr	In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels.
406	Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors	Felix Heide, Lei Xiao, Wolfgang Heidrich, Matthias B. Hullin	In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors.
407	Fourier Analysis on Transient Imaging with a Multifrequency Time-of-Flight Camera	Jingyu Lin, Yebin Liu, Matthias B. Hullin, Qionghai Dai	In this paper we discover that the data captured by a multifrequency time-of-flight (ToF) camera is the Fourier transform of a transient image, and identify the sources of systematic error.
408	Transparent Object Reconstruction via Coded Transport of Intensity	Chenguang Ma, Xing Lin, Jinli Suo, Qionghai Dai, Gordon Wetzstein	In this paper, we derive an intuitive formulation of light transport in refractive media using light fields and the transport of intensity equation.
409	3D Shape and Indirect Appearance by Structured Light Transport	Matthew O’Toole, John Mather, Kiriakos N. Kutulakos	We consider the problem of deliberately manipulating the direct and indirect light flowing through a time-varying, fully-general scene in order to simplify its visual analysis.
410	Shape-Preserving Half-Projective Warps for Image Stitching	Che-Han Chang, Yoichi Sato, Yung-Yu Chuang	This paper proposes a novel parametric warp which is a spatial combination of a projective transformation and a similarity transformation.
411	Parallax-tolerant Image Stitching	Fan Zhang, Feng Liu	This paper presents a local stitching method to handle parallax based on the observation that input images do not need to be perfectly aligned over the whole overlapping region for stitching.
412	Learning Everything about Anything: Webly-Supervised Visual Concept Learning	Santosh K. Divvala, Ali Farhadi, Carlos Guestrin	In this paper, we introduce a fully-automated approach for learning extensive models for a wide range of variations (e.g. actions, interactions, attributes and beyond) within any concept.
413	Dirichlet-based Histogram Feature Transform for Image Classification	Takumi Kobayashi	In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance.
414	BING: Binarized Normed Gradients for Objectness Estimation at 300fps	Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, Philip Torr	Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure.
415	Context Driven Scene Parsing with Attention to Rare Classes	Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang	This paper presents a scalable scene parsing algorithm based on image retrieval and superpixel matching.
416	Patch to the Future: Unsupervised Visual Prediction	Jacob Walker, Abhinav Gupta, Martial Hebert	In this paper we present a conceptually simple but surprisingly powerful method for visual prediction which combines the effectiveness of mid-level visual elements with temporal modeling.
417	Triangulation Embedding and Democratic Aggregation for Image Search	Herve Jegou, Andrew Zisserman	More specifically we aim to construct a dense representation, like the Fisher Vector or VLAD, though of small or intermediate size.
418	Low-Cost Compressive Sensing for Color Video and Depth	Xin Yuan, Patrick Llull, Xuejun Liao, Jianbo Yang, David J. Brady, Guillermo Sapiro, Lawrence Carin	Low-Cost Compressive Sensing for Color Video and Depth
419	Aliasing Detection and Reduction in Plenoptic Imaging	Zhaolin Xiao, Qing Wang, Guoqing Zhou, Jingyi Yu	In this paper, we present a different solution that first detects and then removes aliasing at the light field refocusing stage.
420	Illumination-Aware Age Progression	Ira Kemelmacher-Shlizerman, Supasorn Suwajanakorn, Steven M. Seitz	We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination.
421	Color Transfer Using Probabilistic Moving Least Squares	Youngbae Hwang, Joon-Young Lee, In So Kweon, Seon Joo Kim	This paper introduces a new color transfer method which is a process of transferring color of an image to match the color of another image of the same scene.
422	Image Pre-compensation: Balancing Contrast and Ringing	Yu Ji, Jinwei Ye, Sing Bing Kang, Jingyi Yu	In this paper, we show how global tone mapping functions affect contrast and ringing in image pre-compensation.
423	Time-Mapping Using Space-Time Saliency	Feng Zhou, Sing Bing Kang, Michael F. Cohen	We describe a new approach for generating regular-speed, low-frame-rate (LFR) video from a high-frame-rate (HFR) input while preserving the important moments in the original.
424	Gyro-Based Multi-Image Deconvolution for Removing Handshake Blur	Sung Hee Park, Marc Levoy	In this paper we analyze multi-image approaches, which capture and combine multiple frames in order to make deblurring more robust and tractable.
425	Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution	Jing Li, Zhichao Lu, Gang Zeng, Rui Gan, Hongbin Zha	This paper describes a patchwork assembly algorithm for depth image super-resolution.
426	Deblurring Low-light Images with Light Streaks	Zhe Hu, Sunghyun Cho, Jue Wang, Ming-Hsuan Yang	In this work, we propose a new method that utilizes light streaks to help deblur low-light images.
427	Depth Enhancement via Low-rank Matrix Completion	Si Lu, Xiaofeng Ren, Feng Liu	In this paper, we present a depth map enhancement algorithm that performs depth map completion and de-noising simultaneously.
428	Raw-to-Raw: Mapping between Image Sensor Color Responses	Rang Nguyen, Dilip K. Prasad, Michael S. Brown	To address this issue, we introduce an illumination-independent mapping approach that uses white-balancing to assist in reducing the number of required transformations.
429	DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences	Hongsheng Yang, Wen-Yan Lin, Jiangbo Lu	This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task.
430	Robust 3D Tracking with Descriptor Fields	Alberto Crivellaro, Vincent Lepetit	We introduce a method that can register challenging images from specular and poorly textured 3D environments, on which previous approaches fail.
431	Evolutionary Quasi-random Search for Hand Articulations Tracking	Iason Oikonomidis, Manolis I.A. Lourakis, Antonis A. Argyros	We present a new method for tracking the 3D position, global orientation and full articulation of human hands.
432	Scalable 3D Tracking of Multiple Interacting Objects	Nikolaos Kyriazis, Antonis Argyros	We propose a middle ground, namely an Ensemble of Collaborative Trackers (ECT), that combines best traits from both worlds to deliver a practical and accurate solution to the multi-object 3D tracking problem.
433	Bayesian Active Appearance Models	Joan Alabort-i-Medina, Stefanos Zafeiriou	To this end, we use a simple probabilistic model for texture generation assuming both Gaussian noise and a Gaussian prior over a latent texture space.
434	Human Shape and Pose Tracking Using Keyframes	Chun-Hao Huang, Edmond Boyer, Nassir Navab, Slobodan Ilic	We propose to use key poses of the tracked person as multiple reference models.
435	Better Feature Tracking Through Subspace Constraints	Bryan Poling, Gilad Lerman, Arthur Szlam	We present a framework for jointly tracking a set of features, which enables sharing information between the different features in the scene.
436	Online Object Tracking, Learning and Parsing with And-Or Graphs	Yang Lu, Tianfu Wu, Song Chun Zhu	This paper presents a framework for simultaneously tracking, learning and parsing objects with a hierarchical and compositional And-Or graph (AOG) representation.
437	Region-based Particle Filter for Video Object Segmentation	David Varas, Ferran Marques	We present a video object segmentation approach that extends the particle filter to a region-based image representation.
438	Visual Tracking via Probability Continuous Outlier Model	Dong Wang, Huchuan Lu	In this paper, we present a novel online visual tracking method based on linear representation.
439	Visual Tracking Using Pertinent Patch Selection and Masking	Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim	A novel visual tracking algorithm using patch-based appearance models is proposed in this paper.
440	Interval Tracker: Tracking by Interval Analysis	Junseok Kwon, Kyoung Mu Lee	This paper proposes a robust tracking method that uses interval analysis.
441	Unifying Spatial and Attribute Selection for Distracter-Resilient Tracking	Nan Jiang, Ying Wu	This paper presents a novel method to jointly determine the best spatial location and the optimal metric.
442	Pedestrian Detection in Low-resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)	Jiejie Zhu, Omar Javed, Jingen Liu, Qian Yu, Hui Cheng, Harpreet Sawhney	To overcome these challenges, we propose a novel approach to extract Multi-scale Intrinsic Motion Structure features from pedestrian’s motion patterns for pedestrian detection.
443	Multi-target Tracking with Motion Context in Tensor Power Iteration	Xinchu Shi, Haibin Ling, Weiming Hu, Chunfeng Yuan, Junliang Xing	In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization.
444	SphereFlow: 6 DoF Scene Flow from RGB-D Pairs	Michael Hornacek, Andrew Fitzgibbon, Carsten Rother	We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames.
445	Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow	Linchao Bao, Qingxiong Yang, Hailin Jin	We present a fast optical flow algorithm that can handle large displacement motions.
446	Learning an Image-based Motion Context for Multiple People Tracking	Laura Leal-Taixe, Michele Fenzi, Alina Kuznetsova, Bodo Rosenhahn, Silvio Savarese	We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals.
447	Semi-Supervised Coupled Dictionary Learning for Person Re-identification	Xiao Liu, Mingli Song, Dacheng Tao, Xingchen Zhou, Chun Chen, Jiajun Bu	In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images.
448	What are You Talking About? Text-to-Image Coreference	Chen Kong, Dahua Lin, Mohit Bansal, Raquel Urtasun, Sanja Fidler	In this paper we exploit natural sentential descriptions of RGB-D scenes in order to improve 3D semantic parsing.
449	Predicting Failures of Vision Systems	Peng Zhang, Jiuling Wang, Ali Farhadi, Martial Hebert, Devi Parikh	In this work, we hope to draw the community’s attention to the latter, which is arguably equally problematic for real applications.
450	Three Guidelines of Online Learning for Large-Scale Visual Recognition	Yoshitaka Ushiku, Masatoshi Hidaka, Tatsuya Harada	In this paper, we would like to evaluate online learning algorithms for large-scale visual recognition using state-of-the-art features which are preselected and held fixed.
451	Using k-Poselets for Detecting People and Localizing Their Keypoints	Georgia Gkioxari, Bharath Hariharan, Ross Girshick, Jitendra Malik	It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.
452	Randomized Max-Margin Compositions for Visual Recognition	Angela Eigenstetter, Masato Takami, Bjorn Ommer	In contrast to the common rationale of compositional approaches, we do not aim for semantically meaningful ensembles.
453	Large-Scale Visual Font Recognition	Guang Chen, Jianchao Yang, Hailin Jin, Jonathan Brandt, Eli Shechtman, Aseem Agarwala, Tony X. Han	As font recognition is inherently dynamic and open-ended, i.e., new classes and data for existing categories are constantly added to the database over time, we propose a scalable solution based on the nearest class mean classifier (NCM). To address the VFR problem, we construct a large-scale dataset containing 2,420 font classes, which easily exceeds the scale of most image categorization datasets in computer vision.
454	Describing Textures in the Wild	Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi	Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes.
455	Relative Parts: Distinctive Parts for Learning Relative Attributes	Ramachandruni N. Sandeep, Yashaswi Verma, C. V. Jawahar	In this paper, we extend this idea towards learning relative attributes using local parts that are shared across categories.
456	Understanding Objects in Detail with Fine-Grained Attributes	Andrea Vedaldi, Siddharth Mahendran, Stavros Tsogkas, Subhransu Maji, Ross Girshick, Juho Kannala, Esa Rahtu, Iasonas Kokkinos, Matthew B. Blaschko, David Weiss, Ben Taskar, Karen Simonyan, Naomi Saphra, Sammy Mohamed	We study the problem of understanding objects in detail, intended as recognizing a wide array of fine-grained object attributes. To this end, we introduce a dataset of 7,413 airplanes annotated in detail with parts and their attributes, leveraging images donated by airplane spotters and crowdsourcing both the design and collection of the detailed annotations.
457	Predicting User Annoyance Using Visual Attributes	Gordon Christie, Amar Parkash, Ujwal Krothapalli, Devi Parikh	To avoid having to conduct extensive user studies to gather the annoyance matrix for all possible mistakes, we propose predicting the annoyance of previously unseen mistakes by learning from example mistakes and their corresponding annoyance.
458	Linear Ranking Analysis	Weihong Deng, Jiani Hu, Jun Guo	We extend the classical linear discriminant analysis (LDA) technique to linear ranking analysis (LRA), by considering the ranking order of classes centroids on the projected subspace.
459	Transformation Pursuit for Image Classification	Mattis Paulin, Jerome Revaud, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid	We propose a principled algorithm Image Transformation Pursuit (ITP) for the automatic selection of a compact set of transformations.
460	Incremental Learning of NCM Forests for Large-Scale Image Classification	Marko Ristin, Matthieu Guillaumin, Juergen Gall, Luc Van Gool	To remedy this, we introduce Nearest Class Mean Forests (NCMF), a variant of Random Forests where the decision nodes are based on nearest class mean (NCM) classification.
461	Object Classification with Adaptable Regions	Hakan Bilen, Marco Pedersoli, Vinay P. Namboodiri, Tinne Tuytelaars, Luc Van Gool	To achieve our objective, we propose a new latent SVM model for category level object classification.
462	Discriminative Ferns Ensemble for Hand Pose Recognition	Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak	We present the Discriminative Ferns Ensemble (DFE) classifier for efficient visual object recognition.
463	Are Cars Just 3D Boxes? – Jointly Estimating the 3D Shape of Multiple Objects	Muhammad Zeeshan Zia, Michael Stark, Konrad Schindler	In this paper, we approach the problem of scene understanding from the perspective of 3D shape modeling, and design a 3D scene representation that reasons jointly about the 3D shape of multiple objects.
464	2D Human Pose Estimation: New Benchmark and State of the Art Analysis	Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, Bernt Schiele	In this paper we introduce a novel benchmark “MPII Human Pose” that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels.
465	Using a Deformation Field Model for Localizing Faces and Facial Points under Weak Supervision	Marco Pedersoli, Tinne Tuytelaars, Luc Van Gool	In this work we extend the mixtures from trees to more general loopy graphs.
466	Active Annotation Translation	Steve Branson, Kristjan Eldjarn Hjorleifsson, Pietro Perona	We introduce a general framework for quickly annotating an image dataset when previous annotations exist.
467	Looking Beyond the Visible Scene	Aditya Khosla, Byoungkwon An An, Joseph J. Lim, Antonio Torralba	In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels – it is so much more.
468	Two-Class Weather Classification	Cewu Lu, Di Lin, Jiaya Jia, Chi-Keung Tang	Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy. We build a new weather image dataset consisting of 10K sunny and cloudy images, which is available online together with the executable.
469	Learning Important Spatial Pooling Regions for Scene Classification	Di Lin, Cewu Lu, Renjie Liao, Jiaya Jia	We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification.
470	Orientational Pyramid Matching for Recognizing Indoor Scenes	Lingxi Xie, Jingdong Wang, Baining Guo, Bo Zhang, Qi Tian	In this paper, we introduce an alternative approach, Orientational Pyramid Matching (OPM), for orientational context modeling.
471	Multilabel Ranking with Inconsistent Rankers	Xin Geng, Longrun Luo	The proposed method mainly includes two steps.
472	Scene Parsing with Object Instances and Occlusion Ordering	Joseph Tighe, Marc Niethammer, Svetlana Lazebnik	This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships.
473	A Riemannian Framework for Matching Point Clouds Represented by the Schrodinger Distance Transform	Yan Deng, Anand Rangarajan, Stephan Eisenschenk, Baba C. Vemuri	In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrodinger distance transform (SDT) representation.
474	Seeing 3D Chairs: Exemplar Part-based 2D-3D Alignment using a Large Dataset of CAD Models	Mathieu Aubry, Daniel Maturana, Alexei A. Efros, Bryan C. Russell, Josef Sivic	This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online.
475	A Mixture of Manhattan Frames: Beyond the Manhattan World	Julian Straub, Guy Rosman, Oren Freifeld, John J. Leonard, John W. Fisher III	We propose a novel probabilistic model that describes the world as a mixture of Manhattan frames: each frame defines a different orthogonal coordinate system.
476	Local Regularity-driven City-scale Facade Detection from Aerial Images	Jingchen Liu, Yanxi Liu	We propose a novel regularity-driven framework for facade detection from aerial images of urban scenes.
477	Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture	Danhang Tang, Hyung Jin Chang, Alykhan Tejani, Tae-Kyun Kim	In this paper we present the Latent Regression Forest (LRF), a novel framework for real-time, 3D hand pose estimation from a single depth image.
478	FAUST: Dataset and Evaluation for 3D Mesh Registration	Federica Bogo, Javier Romero, Matthew Loper, Michael J. Black	We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments.
479	Optimizing Over Radial Kernels on Compact Manifolds	Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi	We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification.
480	Grassmann Averages for Scalable Robust PCA	Soren Hauberg, Aasa Feragen, Michael J. Black	To address this, we introduce the Grassmann Average (GA), which expresses dimensionality reduction as an average of the subspaces spanned by the data.
481	Robust Subspace Segmentation with Block-diagonal Prior	Jiashi Feng, Zhouchen Lin, Huan Xu, Shuicheng Yan	The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix.
482	Unsupervised One-Class Learning for Automatic Outlier Removal	Wei Liu, Gang Hua, John R. Smith	In this paper, we propose a novel one-class learning approach which is robust to contamination of input training data and able to discover the outliers that corrupt one class of data source.
483	Smooth Representation Clustering	Han Hu, Zhouchen Lin, Jianjiang Feng, Jie Zhou	In this paper, we analyze the grouping effect of representation based methods in depth.
484	Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD	Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, Misha Kilmer	In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4- D (color) video data completion and de-noising.
485	Second-Order Shape Optimization for Geometric Inverse Problems in Vision	Jonathan Balzer, Stefano Soatto	We develop a method for optimization in shape spaces, i.e., sets of surfaces modulo re-parametrization.
486	l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence	Chenglong Bao, Hui Ji, Yuhui Quan, Zuowei Shen	In this paper, we proposed a fast proximal method for solving l0 norm based dictionary learning problems, and we proved that the whole sequence generated by the proposed method converges to a stationary point with sub-linear convergence rate.
487	Adaptive Partial Differential Equation Learning for Visual Saliency Detection	Risheng Liu, Junjie Cao, Zhouchen Lin, Shiguang Shan	Instead of designing PDEs with fixed formulation and boundary condition, this paper proposes a novel framework for adaptively learning a PDE system from an image for visual saliency detection.
488	Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-rank Matrices	Xianbiao Shu, Fatih Porikli, Narendra Ahuja	In this paper, we propose a Robust Orthogonal Subspace Learning (ROSL) method to achieve efficient low-rank recovery.
489	Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos	Gunhee Kim, Eric P. Xing	In this paper, we investigate an approach for reconstructing storyline graphs from large-scale collections of Internet images, and optionally other side information such as friendship graphs.
490	Active Flattening of Curved Document Images via Two Structured Beams	Gaofeng Meng, Ying Wang, Shenquan Qu, Shiming Xiang, Chunhong Pan	In this paper,we propose an active method to correct geometric distortions in a camera-captured document image.
491	Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models	Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars	We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class.
492	Bayesian View Synthesis and Image-Based Rendering Principles	Sergi Pujades, Frederic Devernay, Bastian Goldluecke	In this paper, we address the problem of synthesizing novel views from a set of input images.
493	Fast MRF Optimization with Application to Depth Reconstruction	Qifeng Chen, Vladlen Koltun	We describe a simple and fast algorithm for optimizing Markov random fields over images.
494	Exploiting Shading Cues in Kinect IR Images for Geometry Refinement	Gyeongmin Choe, Jaesik Park, Yu-Wing Tai, In So Kweon	In this paper, we propose a method to refine geometry of 3D meshes from the Kinect fusion by exploiting shading cues captured from the infrared (IR) camera of Kinect.
495	Fast Rotation Search with Stereographic Projections for 3D Registration	Alvaro Parra Bustos, Tat-Jun Chin, David Suter	In this work, assuming that the translation parameters are known, we focus on constructing a fast rotation search algorithm.
496	Local Readjustment for High-Resolution 3D Reconstruction	Siyu Zhu, Tian Fang, Jianxiong Xiao, Long Quan	To this end, we propose a segment-based approach to readjust the camera poses locally and improve the reconstruction for fine geometry details.
497	Turning Mobile Phones into 3D Scanners	Kalin Kolev, Petri Tanskanen, Pablo Speciale, Marc Pollefeys	In this paper, we propose an efficient and accurate scheme for the integration of multiple stereo-based depth measurements.
498	T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting	Luca Magri, Andrea Fusiello	This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers.
499	Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement	Tak-Wai Hui, King Ngi Ngan	Since the spatial resolution of the color image is generally higher than that of the depth image, this paper introduces a new method to enhance the depth images captured by a moving RGB-D system using the depth cues from the induced optical flow.
500	Generalized Pupil-Centric Imaging and Analytical Calibration for a Non-frontal Camera	Avinash Kumar, Narendra Ahuja	In this paper, we focus on rotation based non-frontal camera calibration and address the aforementioned problems of over-parameterization and inadequacy of existing pupil-centric imaging model.
501	Geometric Urban Geo-Localization	Mayank Bansal, Kostas Daniilidis	We propose a purely geometric correspondence-free approach to urban geo-localization using 3D point-ray features extracted from the Digital Elevation Map of an urban environment.
502	3D Reconstruction from Accidental Motion	Fisher Yu, David Gallup	We have discovered that 3D reconstruction can be achieved from asingle still photographic capture due to accidental motions of thephotographer, even while attempting to hold the camera still.
503	Real-time Model-based Articulated Object Pose Detection and Tracking with Variable Rigidity Constraints	Karl Pauwels, Leonardo Rubio, Eduardo Ros	We propose a novel rigidization framework for optimally handling unobservable parts during tracking.
504	Occluding Contours for Multi-View Stereo	Qi Shan, Brian Curless, Yasutaka Furukawa, Carlos Hernandez, Steven M. Seitz	This paper leverages occluding contours (aka “internal silhouettes”) to improve the performance of multi-view stereo methods.
505	Aerial Reconstructions via Probabilistic Data Fusion	Randi Cabezas, Oren Freifeld, Guy Rosman, John W. Fisher III	We propose an integrated probabilistic model for multi-modal fusion of aerial imagery, LiDAR data, and (optional) GPS measurements.
506	3D Modeling from Wide Baseline Range Scans using Contour Coherence	Ruizhe Wang, Jongmoo Choi, Gerard Medioni	We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization.
507	Ground Plane Estimation using a Hidden Markov Model	Ralf Dragon, Luc Van Gool	We focus on the problem of estimating the ground plane orientation and location in monocular video sequences from a moving observer.
508	Orientation Robust Text Line Detection in Natural Images	Le Kang, Yi Li, David Doermann	In this paper, higher-order correlation clustering (HOCC) is used for text line detection in natural images.
509	Strokelets: A Learned Multi-Scale Representation for Scene Text Recognition	Cong Yao, Xiang Bai, Baoguang Shi, Wenyu Liu	In this paper, we propose a novel multi-scale representation for scene text recognition.
510	Region-based Discriminative Feature Pooling for Scene Text Recognition	Chen-Yu Lee, Anurag Bhardwaj, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu	We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition.
511	Fast and Exact: ADMM-Based Discriminative Shape Segmentation with Loopy Part Models	Haithem Boussaid, Iasonas Kokkinos	In this work we use loopy part models to segment ensembles of organs in medical images.
512	Pseudoconvex Proximal Splitting for L-infty Problems in Multiview Geometry	Anders Eriksson, Mats Isaksson	In this paper we study optimization methods for minimizing large-scale pseudoconvex L_infinity problems in multiview geometry.
513	A Convex Relaxation of the Ambrosio–Tortorelli Elliptic Functionals for the Mumford-Shah Functional	Youngwook Kee, Junmo Kim	In this paper, we revisit the phase-field approximation of Ambrosio and Tortorelli for the Mumford–Shah functional.
514	Sequential Convex Relaxation for Mutual Information-Based Unsupervised Figure-Ground Segmentation	Youngwook Kee, Mohamed Souiai, Daniel Cremers, Junmo Kim	We propose an optimization algorithm for mutual-information-based unsupervised figure-ground separation.
515	Decorrelated Vectorial Total Variation	Shunsuke Ono, Isao Yamada	This paper proposes a new vectorial total variation prior (VTV) for color images.
516	Efficient Squared Curvature	Claudia Nieuwenhuis, Eno Toeppe, Lena Gorelick, Olga Veksler, Yuri Boykov	We derive a new model for computing squared curvature based on integral geometry.
517	Multi-feature Spectral Clustering with Minimax Optimization	Hongxing Wang, Chaoqun Weng, Junsong Yuan	In this paper, we propose a novel formulation for multi-feature clustering using minimax optimization.
518	Quality-based Multimodal Classification using Tree-Structured Sparsity	Soheil Bahrampour, Asok Ray, Nasser M. Nasrabadi, Kenneth W. Jenkins	An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information.
519	Newton Greedy Pursuit: A Quadratic Approximation Method for Sparsity-Constrained Optimization	Xiao-Tong Yuan, Qingshan Liu	Inspired by the classic constrained Newton method, we propose in this paper the NewTon Greedy Pursuit (NTGP) method to approximately minimizes a twice differentiable function over sparsity constraint.
520	Generalized Nonconvex Nonsmooth Low-Rank Minimization	Canyi Lu, Jinhui Tang, Shuicheng Yan, Zhouchen Lin	Based on this property, we propose an Iteratively Reweighted Nuclear Norm (IRNN) algorithm to solve the nonconvex nonsmooth low-rank minimization problem.
521	Latent Dictionary Learning for Sparse Representation based Classification	Meng Yang, Dengxin Dai, Lilin Shen, Luc Van Gool	More specifically, we introduce a latent representation model, in which discrimination of the learned dictionary is exploited via minimizing the within-class scatter of coding coefficients and the latent-value weighted dictionary coherence.
522	Is Rotation a Nuisance in Shape Recognition?	Qiuhong Ke, Yi Li	In this paper we address three fundamental issues brought by rotation in shapes: 1) is alignment among shapes necessary?
523	Dual-Space Decomposition of 2D Complex Shapes	Guilin Liu, Zhonghua Xi, Jyh-Ming Lien	In this paper, we propose a new decomposition method, called Dual-space Decomposition that handles complex 2D shapes by recognizing the importance of holes and classifying holes as either topological noise or structurally important features.
524	Noising versus Smoothing for Vertex Identification in Unknown Shapes	Konstantinos A. Raftopoulos, Marin Ferecatu	A method for identifying shape features of local nature on the shape’s boundary, in a way that is facilitated by the presence of noise is presented.
525	Surface Registration by Optimization in Constrained Diffeomorphism Space	Wei Zeng, Lok Ming Lui, Xianfeng Gu	This work proposes a novel framework for optimization in the constrained diffeomorphism space for deformable surface registration.
526	Dense Non-Rigid Shape Correspondence using Random Forests	Emanuele Rodola, Samuel Rota Bulo, Thomas Windheuser, Matthias Vestner, Daniel Cremers	We propose a shape matching method that produces dense correspondences tuned to a specific class of shapes and deformations.
527	Covariance Descriptors for 3D Shape Matching and Retrieval	Hedi Tabia, Hamid Laga, David Picard, Philippe-Henri Gosselin	In this paper we propose a novel method for 3D shape analysis using the covariance matrices of the descriptors rather than the descriptors themselves.
528	Symmetry-Aware Nonrigid Matching of Incomplete 3D Surfaces	Yusuke Yoshiyasu, Eiichi Yoshida, Kazuhito Yokoi, Ryusuke Sagawa	We present a nonrigid shape matching technique for establishing correspondences of incomplete 3D surfaces that exhibit intrinsic reflectional symmetry.
529	An Automated Estimator of Image Visual Realism Based on Human Cognition	Shaojing Fan, Tian-Tsong Ng, Jonathan S. Herberg, Bryan L. Koenig, Cheston Y.-C. Tan, Rangding Wang	In this paper we systematically evaluate factors underlying human perception of visual realism and use that information to create an automated assessment of visual realism.
530	SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization	Shuaicheng Liu, Lu Yuan, Ping Tan, Jian Sun	We propose a novel motion model, SteadyFlow, to represent the motion between neighboring video frames for stabilization.
531	Automatic Face Reenactment	Pablo Garrido, Levi Valgaerts, Ole Rehmsen, Thorsten Thormahlen, Patrick Perez, Christian Theobalt	We propose an image-based, facial reenactment system that replaces the face of an actor in an existing target video with the face of a user from a source video, while preserving the original target performance.
532	Joint Summarization of Large-scale Collections of Web Images and Videos for Storyline Reconstruction	Gunhee Kim, Leonid Sigal, Eric P. Xing	In this paper, we address the problem of jointly summarizing large sets of Flickr images and YouTube videos. For evaluation, we collect the datasets of 20 outdoor activities, consisting of 2.7M Flickr images and 16K YouTube videos.
533	Semi-supervised Relational Topic Model for Weakly Annotated Image Recognition in Social Media	Zhenxing Niu, Gang Hua, Xinbo Gao, Qi Tian	In this paper, we address the problem of recognizing images with weakly annotated text tags.
534	Beyond Human Opinion Scores: Blind Image Quality Assessment based on Synthetic Scores	Peng Ye, Jayant Kumar, David Doermann	This paper proposes BLISS (Blind Learning of Image Quality using Synthetic Scores).
535	Active Sampling for Subjective Image Quality Assessment	Peng Ye, David Doermann	We present a hybrid subjective test which combines MOS and PC tests via a unified probabilistic model and an active sampling method.
536	A Study on Cross-Population Age Estimation	Guodong Guo, Chao Zhang	In this paper we propose novel methods for cross-population age estimation with a good performance.
537	Remote Heart Rate Measurement From Face Videos Under Realistic Situations	Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikainen	Those methods work well on stationary subjects under well controlled conditions, but their performance significantly degrades if the videos are recorded under more challenging conditions, specifically when subjects’ motions and illumination variations are involved.
538	6 Seconds of Sound and Vision: Creativity in Micro-Videos	Miriam Redi, Neil O’Hare, Rossano Schifanella, Michele Trevisiol, Alejandro Jaimes	In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video.
539	GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor	Amir Roshan Zamir, Shervin Ardeshir, Mubarak Shah	In this paper, we propose a method for addressing this problem.