cvpr cvpr2013 knowledge-graph by maker-knowledge-mining
1 cvpr-2013-3D-Based Reasoning with Blocks, Support, and Stability
Author: Zhaoyin Jia, Andrew Gallagher, Ashutosh Saxena, Tsuhan Chen
Abstract: 3D volumetric reasoning is important for truly understanding a scene. Humans are able to both segment each object in an image, and perceive a rich 3D interpretation of the scene, e.g., the space an object occupies, which objects support other objects, and which objects would, if moved, cause other objects to fall. We propose a new approach for parsing RGB-D images using 3D block units for volumetric reasoning. The algorithm fits image segments with 3D blocks, and iteratively evaluates the scene based on block interaction properties. We produce a 3D representation of the scene based on jointly optimizing over segmentations, block fitting, supporting relations, and object stability. Our algorithm incorporates the intuition that a good 3D representation of the scene is the one that fits the data well, and is a stable, self-supporting (i.e., one that does not topple) arrangement of objects. We experiment on several datasets including controlled and real indoor scenarios. Results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation.
2 cvpr-2013-3D Pictorial Structures for Multiple View Articulated Pose Estimation
Author: Magnus Burenius, Josephine Sullivan, Stefan Carlsson
Abstract: We consider the problem of automatically estimating the 3D pose of humans from images, taken from multiple calibrated views. We show that it is possible and tractable to extend the pictorial structures framework, popular for 2D pose estimation, to 3D. We discuss how to use this framework to impose view, skeleton, joint angle and intersection constraints in 3D. The 3D pictorial structures are evaluated on multiple view data from a professional football game. The evaluation is focused on computational tractability, but we also demonstrate how a simple 2D part detector can be plugged into the framework.
3 cvpr-2013-3D R Transform on Spatio-temporal Interest Points for Action Recognition
Author: Chunfeng Yuan, Xi Li, Weiming Hu, Haibin Ling, Stephen Maybank
Abstract: Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignorespotentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the ℛ transform which is defined as an extended 3D discrete Rℛa tdroann transform, followed by applying a tewdo 3-dDir decitsicorneatel two-dimensional principal component analysis. Such ℛ feature captures the geometrical information of the Sinuctehre ℛst points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the ℛ feature with the BOVW representation for further improving recognition accuracy. Wpree suetnilitzaea context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition.
4 cvpr-2013-3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image
Author: Ishani Chakraborty, Hui Cheng, Omar Javed
Abstract: We present a unified framework for detecting and classifying people interactions in unconstrained user generated images. 1 Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then extract spatial configuration features from explicit 3D people positions. This approach has several advantages. First, it can accurately estimate relative distances and orientations between people in 3D. Second, it encodes spatial arrangements of people into a richer set of shape descriptors than afforded in 2D. Our 3D shape descriptors are invariant to camera pose variations often seen in web images and videos. The proposed approach also estimates camera pose and uses it to capture the intent of the photo. To achieve accurate 3D people layout estimation, we develop an algorithm that robustly fuses semantic constraints about human interpositions into a linear camera model. This enables our model to handle large variations in people size, heights (e.g. age) and poses. An accurate 3D layout also allows us to construct features informed by Proxemics that improves our semantic classification. To characterize the human interaction space, we introduce visual proxemes; a set of prototypical patterns that represent commonly occurring social interactions in events. We train a discriminative classifier that classifies 3D arrangements of people into visual proxemes and quantitatively evaluate the performance on a large, challenging dataset.
5 cvpr-2013-A Bayesian Approach to Multimodal Visual Dictionary Learning
Author: Go Irie, Dong Liu, Zhenguo Li, Shih-Fu Chang
Abstract: Despite significant progress, most existing visual dictionary learning methods rely on image descriptors alone or together with class labels. However, Web images are often associated with text data which may carry substantial information regarding image semantics, and may be exploited for visual dictionary learning. This paper explores this idea by leveraging relational information between image descriptors and textual words via co-clustering, in addition to information of image descriptors. Existing co-clustering methods are not optimal for this problem because they ignore the structure of image descriptors in the continuous space, which is crucial for capturing visual characteristics of images. We propose a novel Bayesian co-clustering model to jointly estimate the underlying distributions of the continuous image descriptors as well as the relationship between such distributions and the textual words through a unified Bayesian inference. Extensive experiments on image categorization and retrieval have validated the substantial value of the proposed joint modeling in improving visual dictionary learning, where our model shows superior performance over several recent methods.
Author: Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Jan Lellmann, Nikos Komodakis, Carsten Rother
Abstract: Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of random field models means that the kinds of inference problems we solve have changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of 24 state-of-art techniques on a corpus of 2,300 energy minimization instances from 20 diverse computer vision applications. To ensure reproducibility, we evaluate all methods in the OpenGM2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.
7 cvpr-2013-A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit
Author: Yan Pan, Hanjiang Lai, Cong Liu, Shuicheng Yan
Abstract: Data fusion, which effectively fuses multiple prediction lists from different kinds of features to obtain an accurate model, is a crucial component in various computer vision applications. Robust late fusion (RLF) is a recent proposed method that fuses multiple output score lists from different models via pursuing a shared low-rank latent matrix. Despite showing promising performance, the repeated full Singular Value Decomposition operations in RLF’s optimization algorithm limits its scalability in real world vision datasets which usually have large number of test examples. To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-andconquer method. The proposed method divides the original low-rank latent matrix learning problem into two sizereduced subproblems, which may be solved via any base algorithm, and combines the results from the subproblems to obtain the final solution. Our theoretical analysis shows that withfixedprobability, theproposed divide-and-conquer method has recovery guarantees comparable to those of its base algorithm. Moreover, we develop an efficient base algorithm for the corresponding subproblems by factorizing a large matrix into the product of two size-reduced matrices. We also provide high probability recovery guarantees of the base algorithm. The proposed method is evaluated on various fusion problems in object categorization and video event detection. Under comparable accuracy, the proposed method performs more than 180 times faster than the stateof-the-art baselines on the CCV dataset with about 4,500 test examples for video event detection.
8 cvpr-2013-A Fast Approximate AIB Algorithm for Distributional Word Clustering
Author: Lei Wang, Jianjia Zhang, Luping Zhou, Wanqing Li
Abstract: Distributional word clustering merges the words having similar probability distributions to attain reliable parameter estimation, compact classification models and even better classification performance. Agglomerative Information Bottleneck (AIB) is one of the typical word clustering algorithms and has been applied to both traditional text classification and recent image recognition. Although enjoying theoretical elegance, AIB has one main issue on its computational efficiency, especially when clustering a large number of words. Different from existing solutions to this issue, we analyze the characteristics of its objective function the loss of mutual information, and show that by merely using the ratio of word-class joint probabilities of each word, good candidate word pairs for merging can be easily identified. Based on this finding, we propose a fast approximate AIB algorithm and show that it can significantly improve the computational efficiency of AIB while well maintaining or even slightly increasing its classification performance. Experimental study on both text and image classification benchmark data sets shows that our algorithm can achieve more than 100 times speedup on large real data sets over the state-of-the-art method.
9 cvpr-2013-A Fast Semidefinite Approach to Solving Binary Quadratic Problems
Author: Peng Wang, Chunhua Shen, Anton van_den_Hengel
Abstract: Many computer vision problems can be formulated as binary quadratic programs (BQPs). Two classic relaxation methods are widely used for solving BQPs, namely, spectral methods and semidefinite programming (SDP), each with their own advantages and disadvantages. Spectral relaxation is simple and easy to implement, but its bound is loose. Semidefinite relaxation has a tighter bound, but its computational complexity is high for large scale problems. We present a new SDP formulation for BQPs, with two desirable properties. First, it has a similar relaxation bound to conventional SDP formulations. Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods. Extensive experiments on various applications including clustering, image segmentation, co-segmentation and registration demonstrate the usefulness of our SDP formulation for solving large-scale BQPs.
10 cvpr-2013-A Fully-Connected Layered Model of Foreground and Background Flow
Author: Deqing Sun, Jonas Wulff, Erik B. Sudderth, Hanspeter Pfister, Michael J. Black
Abstract: Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another. Traditional layered motion methods, however, employ fairly weak models of scene structure, relying on locally connected Ising/Potts models which have limited ability to capture long-range correlations in natural scenes. To address this, we formulate a fully-connected layered model that enables global reasoning about the complicated segmentations of real objects. Optimization with fully-connected graphical models is challenging, and our inference algorithm leverages recent work on efficient mean field updates for fully-connected conditional random fields. These methods can be implemented efficiently using high-dimensional Gaussian filtering. We combine these ideas with a layered flow model, and find that the long-range connections greatly improve segmentation into figure-ground layers when compared with locally connected MRF models. Experiments on several benchmark datasets show that the method can re- cover fine structures and large occlusion regions, with good flow accuracy and much lower computational cost than previous locally-connected layered models.
11 cvpr-2013-A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles
Author: Dror Sholomon, Omid David, Nathan S. Netanyahu
Abstract: In thispaper wepropose thefirst effective automated, genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two ”parent” solutions to an improved ”child” solution by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-the-art performance solving previously attempted puzzles faster and far more accurately, and also puzzles of size never before attempted. Other contributions include the creation of a benchmark of large images, previously unavailable. We share the data sets and all of our results for future testing and comparative evaluation of jigsaw puzzle solvers.
Author: Michel Antunes, João P. Barreto
Abstract: This article presents a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions using lines detected in images of man-made environments. These two multi-model fitting problems are respectively cast as Uncapacited Facility Location (UFL) and Hierarchical Facility Location (HFL) instances that are efficiently solved using a message passing inference algorithm. We also propose new functions for measuring the consistency between an edge and aputative vanishingpoint, and for computing the vanishing point defined by a subset of edges. Extensive experiments in both synthetic and real images show that our algorithms outperform the state-ofthe-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations that can either share one vanishing direction (Atlanta world) or be completely independent.
13 cvpr-2013-A Higher-Order CRF Model for Road Network Extraction
Author: Jan D. Wegner, Javier A. Montoya-Zegarra, Konrad Schindler
Abstract: The aim of this work is to extract the road network from aerial images. What makes the problem challenging is the complex structure of the prior: roads form a connected network of smooth, thin segments which meet at junctions and crossings. This type of a-priori knowledge is more difficult to turn into a tractable model than standard smoothness or co-occurrence assumptions. We develop a novel CRF formulation for road labeling, in which the prior is represented by higher-order cliques that connect sets of superpixels along straight line segments. These long-range cliques have asymmetric PN-potentials, which express a preference to assign all rather than just some of their constituent superpixels to the road class. Thus, the road likelihood is amplified for thin chains of superpixels, while the CRF is still amenable to optimization with graph cuts. Since the number of such cliques of arbitrary length is huge, we furthermorepropose a sampling scheme which concentrates on those cliques which are most relevant for the optimization. In experiments on two different databases the model significantly improves both the per-pixel accuracy and the topological correctness of the extracted roads, and outper- forms both a simple smoothness prior and heuristic rulebased road completion.
14 cvpr-2013-A Joint Model for 2D and 3D Pose Estimation from a Single Image
Author: Edgar Simo-Serra, Ariadna Quattoni, Carme Torras, Francesc Moreno-Noguer
Abstract: We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.
Author: Peter Welinder, Max Welling, Pietro Perona
Abstract: How many labeled examples are needed to estimate a classifier’s performance on a new dataset? We study the case where data is plentiful, but labels are expensive. We show that by making a few reasonable assumptions on the structure of the data, it is possible to estimate performance curves, with confidence bounds, using a small number of ground truth labels. Our approach, which we call Semisupervised Performance Evaluation (SPE), is based on a generative model for the classifier’s confidence scores. In addition to estimating the performance of classifiers on new datasets, SPE can be used to recalibrate a classifier by reestimating the class-conditional confidence distributions.
16 cvpr-2013-A Linear Approach to Matching Cuboids in RGBD Images
Author: Hao Jiang, Jianxiong Xiao
Abstract: We propose a novel linear method to match cuboids in indoor scenes using RGBD images from Kinect. Beyond depth maps, these cuboids reveal important structures of a scene. Instead of directly fitting cuboids to 3D data, we first construct cuboid candidates using superpixel pairs on a RGBD image, and then we optimize the configuration of the cuboids to satisfy the global structure constraints. The optimal configuration has low local matching costs, small object intersection and occlusion, and the cuboids tend to project to a large region in the image; the number of cuboids is optimized simultaneously. We formulate the multiple cuboid matching problem as a mixed integer linear program and solve the optimization efficiently with a branch and bound method. The optimization guarantees the global optimal solution. Our experiments on the Kinect RGBD images of a variety of indoor scenes show that our proposed method is efficient, accurate and robust against object appearance variations, occlusions and strong clutter.
17 cvpr-2013-A Machine Learning Approach for Non-blind Image Deconvolution
Author: Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling, Bernhard Schölkopf
Abstract: Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant non- blind deconvolution. Currently, the most successful meth- ods involve a regularized inversion of the blur in Fourier domain as a first step. This step amplifies and colors the noise, and corrupts the image information. In a second (and arguably more difficult) step, one then needs to remove the colored noise, typically using a cleverly engineered algorithm. However, the methods based on this two-step ap- proach do not properly address the fact that the image information has been corrupted. In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network. We will show that this approach outperforms the current state-ofthe-art on a large dataset of artificially blurred images. We demonstrate the practical applicability of our method in a real-world example with photographic out-of-focus blur.
18 cvpr-2013-A Max-Margin Riffled Independence Model for Image Tag Ranking
Author: Tian Lan, Greg Mori
Abstract: We propose Max-Margin Riffled Independence Model (MMRIM), a new method for image tag ranking modeling the structured preferences among tags. The goal is to predict a ranked tag list for a given image, where tags are ordered by their importance or relevance to the image content. Our model integrates the max-margin formalism with riffled independence factorizations proposed in [10], which naturally allows for structured learning and efficient ranking. Experimental results on the SUN Attribute and LabelMe datasets demonstrate the superior performance of the proposed model compared with baseline tag ranking methods. We also apply the predicted rank list of tags to several higher-level computer vision applications in image understanding and retrieval, and demonstrate that MMRIM significantly improves the accuracy of these applications.
Author: Yiliang Xu, Sangmin Oh, Anthony Hoogs
Abstract: We present a novel vanishing point detection algorithm for uncalibrated monocular images of man-made environments. We advance the state-of-the-art by a new model of measurement error in the line segment extraction and minimizing its impact on the vanishing point estimation. Our contribution is twofold: 1) Beyond existing hand-crafted models, we formally derive a novel consistency measure, which captures the stochastic nature of the correlation between line segments and vanishing points due to the measurement error, and use this new consistency measure to improve the line segment clustering. 2) We propose a novel minimum error vanishing point estimation approach by optimally weighing the contribution of each line segment pair in the cluster towards the vanishing point estimation. Unlike existing works, our algorithm provides an optimal solution that minimizes the uncertainty of the vanishing point in terms of the trace of its covariance, in a closed-form. We test our algorithm and compare it with the state-of-the-art on two public datasets: York Urban Dataset and Eurasian Cities Dataset. The experiments show that our approach outperforms the state-of-the-art.
20 cvpr-2013-A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems
Author: Byung-Woo Hong, Zhaojin Lu, Ganesh Sundaramoorthi
Abstract: In this work, we address the multi-label Mumford-Shah problem, i.e., the problem of jointly estimating a partitioning of the domain of the image, and functions defined within regions of the partition. We create algorithms that are efficient, robust to undesirable local minima, and are easy-toimplement. Our algorithms are formulated by slightly modifying the underlying statistical model from which the multilabel Mumford-Shah functional is derived. The advantage of this statistical model is that the underlying variables: the labels and thefunctions are less coupled than in the original formulation, and the labels can be computed from the functions with more global updates. The resulting algorithms can be tuned to the desired level of locality of the solution: from fully global updates to more local updates. We demonstrate our algorithm on two applications: joint multi-label segmentation and denoising, and joint multi-label motion segmentation and flow estimation. We compare to the stateof-the-art in multi-label Mumford-Shah problems and show that we achieve more promising results.
21 cvpr-2013-A New Perspective on Uncalibrated Photometric Stereo
22 cvpr-2013-A Non-parametric Framework for Document Bleed-through Removal
23 cvpr-2013-A Practical Rank-Constrained Eight-Point Algorithm for Fundamental Matrix Estimation
24 cvpr-2013-A Principled Deep Random Field Model for Image Segmentation
25 cvpr-2013-A Sentence Is Worth a Thousand Pixels
26 cvpr-2013-A Statistical Model for Recreational Trails in Aerial Images
27 cvpr-2013-A Theory of Refractive Photo-Light-Path Triangulation
29 cvpr-2013-A Video Representation Using Temporal Superpixels
30 cvpr-2013-Accurate Localization of 3D Objects from RGB-D Data Using Segmentation Hypotheses
32 cvpr-2013-Action Recognition by Hierarchical Sequence Summarization
33 cvpr-2013-Active Contours with Group Similarity
34 cvpr-2013-Adaptive Active Learning for Image Classification
35 cvpr-2013-Adaptive Compressed Tomography Sensing
36 cvpr-2013-Adding Unlabeled Samples to Categories by Learned Attributes
37 cvpr-2013-Adherent Raindrop Detection and Removal in Video
39 cvpr-2013-Alternating Decision Forests
40 cvpr-2013-An Approach to Pose-Based Action Recognition
41 cvpr-2013-An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision
43 cvpr-2013-Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs
44 cvpr-2013-Area Preserving Brain Mapping
45 cvpr-2013-Articulated Pose Estimation Using Discriminative Armlet Classifiers
46 cvpr-2013-Articulated and Restricted Motion Subspaces and Their Signatures
47 cvpr-2013-As-Projective-As-Possible Image Stitching with Moving DLT
48 cvpr-2013-Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop
50 cvpr-2013-Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling
51 cvpr-2013-Auxiliary Cuts for General Classes of Higher Order Functionals
52 cvpr-2013-Axially Symmetric 3D Pots Configuration System Using Axis of Symmetry and Break Curve
54 cvpr-2013-BRDF Slices: Accurate Adaptive Anisotropic Appearance Acquisition
55 cvpr-2013-Background Modeling Based on Bidirectional Analysis
56 cvpr-2013-Bayesian Depth-from-Defocus with Shading Constraints
57 cvpr-2013-Bayesian Grammar Learning for Inverse Procedural Modeling
59 cvpr-2013-Better Exploiting Motion for Better Action Recognition
60 cvpr-2013-Beyond Physical Connections: Tree Models in Human Pose Estimation
61 cvpr-2013-Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics
62 cvpr-2013-Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs
63 cvpr-2013-Binary Code Ranking with Weighted Hamming Distance
66 cvpr-2013-Block and Group Regularized Sparse Modeling for Dictionary Learning
67 cvpr-2013-Blocks That Shout: Distinctive Parts for Scene Classification
68 cvpr-2013-Blur Processing Using Double Discrete Wavelet Transform
69 cvpr-2013-Boosting Binary Keypoint Descriptors
70 cvpr-2013-Bottom-Up Segmentation for Top-Down Detection
71 cvpr-2013-Boundary Cues for 3D Object Shape Recovery
72 cvpr-2013-Boundary Detection Benchmarking: Beyond F-Measures
73 cvpr-2013-Bringing Semantics into Focus Using Visual Abstraction
74 cvpr-2013-CLAM: Coupled Localization and Mapping with Efficient Outlier Handling
75 cvpr-2013-Calibrating Photometric Stereo by Holistic Reflectance Symmetry Analysis
76 cvpr-2013-Can a Fully Unconstrained Imaging Model Be Applied Effectively to Central Cameras?
79 cvpr-2013-Cartesian K-Means
81 cvpr-2013-City-Scale Change Detection in Cadastral 3D Models Using Images
83 cvpr-2013-Classification of Tumor Histology via Morphometric Context
84 cvpr-2013-Cloud Motion as a Calibration Cue
85 cvpr-2013-Complex Event Detection via Multi-source Video Attributes
86 cvpr-2013-Composite Statistical Inference for Semantic Segmentation
87 cvpr-2013-Compressed Hashing
88 cvpr-2013-Compressible Motion Fields
89 cvpr-2013-Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation
90 cvpr-2013-Computing Diffeomorphic Paths for Large Motion Interpolation
91 cvpr-2013-Consensus of k-NNs for Robust Neighborhood Selection on Graph-Based Manifolds
92 cvpr-2013-Constrained Clustering and Its Application to Face Clustering in Videos
93 cvpr-2013-Constraints as Features
94 cvpr-2013-Context-Aware Modeling and Recognition of Activities in Video
95 cvpr-2013-Continuous Inference in Graphical Models with Polynomial Energies
96 cvpr-2013-Correlation Filters for Object Alignment
97 cvpr-2013-Correspondence-Less Non-rigid Registration of Triangular Surface Meshes
98 cvpr-2013-Cross-View Action Recognition via a Continuous Virtual Path
99 cvpr-2013-Cross-View Image Geolocalization
100 cvpr-2013-Crossing the Line: Crowd Counting by Integer Programming with Local Features
101 cvpr-2013-Cumulative Attribute Space for Age and Crowd Density Estimation
102 cvpr-2013-Decoding, Calibration and Rectification for Lenselet-Based Plenoptic Cameras
103 cvpr-2013-Decoding Children's Social Behavior
104 cvpr-2013-Deep Convolutional Network Cascade for Facial Point Detection
105 cvpr-2013-Deep Learning Shape Priors for Object Segmentation
106 cvpr-2013-Deformable Graph Matching
107 cvpr-2013-Deformable Spatial Pyramid Matching for Fast Dense Correspondences
108 cvpr-2013-Dense 3D Reconstruction from Severely Blurred Images Using a Single Moving Camera
109 cvpr-2013-Dense Non-rigid Point-Matching Using Random Projections
110 cvpr-2013-Dense Object Reconstruction with Semantic Priors
111 cvpr-2013-Dense Reconstruction Using 3D Object Shape Priors
112 cvpr-2013-Dense Segmentation-Aware Descriptors
113 cvpr-2013-Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video
114 cvpr-2013-Depth Acquisition from Density Modulated Binary Patterns
115 cvpr-2013-Depth Super Resolution by Rigid Body Self-Similarity in 3D
116 cvpr-2013-Designing Category-Level Attributes for Discriminative Visual Recognition
118 cvpr-2013-Detecting Pulse from Head Motions in Video
119 cvpr-2013-Detecting and Aligning Faces by Image Retrieval
120 cvpr-2013-Detecting and Naming Actors in Movies Using Generative Appearance Models
121 cvpr-2013-Detection- and Trajectory-Level Exclusion in Multiple Object Tracking
122 cvpr-2013-Detection Evolution with Multi-order Contextual Co-occurrence
123 cvpr-2013-Detection of Manipulation Action Consequences (MAC)
124 cvpr-2013-Determining Motion Directly from Normal Flows Upon the Use of a Spherical Eye Platform
125 cvpr-2013-Dictionary Learning from Ambiguously Labeled Data
126 cvpr-2013-Diffusion Processes for Retrieval Revisited
128 cvpr-2013-Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space
130 cvpr-2013-Discriminative Color Descriptors
131 cvpr-2013-Discriminative Non-blind Deblurring
132 cvpr-2013-Discriminative Re-ranking of Diverse Segmentations
133 cvpr-2013-Discriminative Segment Annotation in Weakly Labeled Video
134 cvpr-2013-Discriminative Sub-categorization
135 cvpr-2013-Discriminative Subspace Clustering
136 cvpr-2013-Discriminatively Trained And-Or Tree Models for Object Detection
137 cvpr-2013-Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis
138 cvpr-2013-Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition
140 cvpr-2013-Efficient Color Boundary Detection with Color-Opponent Mechanisms
141 cvpr-2013-Efficient Computation of Shortest Path-Concavity for 3D Meshes
142 cvpr-2013-Efficient Detector Adaptation for Object Detection in a Video
143 cvpr-2013-Efficient Large-Scale Structured Learning
144 cvpr-2013-Efficient Maximum Appearance Search for Large-Scale Object Detection
145 cvpr-2013-Efficient Object Detection and Segmentation for Fine-Grained Recognition
146 cvpr-2013-Enriching Texture Analysis with Semantic Data
147 cvpr-2013-Ensemble Learning for Confidence Measures in Stereo Vision
148 cvpr-2013-Ensemble Video Object Cut in Highly Dynamic Scenes
149 cvpr-2013-Evaluation of Color STIPs for Human Action Recognition
150 cvpr-2013-Event Recognition in Videos by Learning from Heterogeneous Web Sources
151 cvpr-2013-Event Retrieval in Large Video Collections with Circulant Temporal Encoding
152 cvpr-2013-Exemplar-Based Face Parsing
153 cvpr-2013-Expanded Parts Model for Human Attribute and Action Recognition in Still Images
154 cvpr-2013-Explicit Occlusion Modeling for 3D Object Class Representations
155 cvpr-2013-Exploiting the Power of Stereo Confidences
156 cvpr-2013-Exploring Compositional High Order Pattern Potentials for Structured Output Learning
157 cvpr-2013-Exploring Implicit Image Statistics for Visual Representativeness Modeling
158 cvpr-2013-Exploring Weak Stabilization for Motion Feature Extraction
159 cvpr-2013-Expressive Visual Text-to-Speech Using Active Appearance Models
162 cvpr-2013-FasT-Match: Fast Affine Template Matching
163 cvpr-2013-Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
164 cvpr-2013-Fast Convolutional Sparse Coding
165 cvpr-2013-Fast Energy Minimization Using Learned State Filters
166 cvpr-2013-Fast Image Super-Resolution Based on In-Place Example Regression
167 cvpr-2013-Fast Multiple-Part Based Object Detection Using KD-Ferns
168 cvpr-2013-Fast Object Detection with Entropy-Driven Evaluation
169 cvpr-2013-Fast Patch-Based Denoising Using Approximated Patch Geodesic Paths
170 cvpr-2013-Fast Rigid Motion Segmentation via Incrementally-Complex Local Models
171 cvpr-2013-Fast Trust Region for Segmentation
172 cvpr-2013-Finding Group Interactions in Social Clutter
173 cvpr-2013-Finding Things: Image Parsing with Regions and Per-Exemplar Detectors
174 cvpr-2013-Fine-Grained Crowdsourcing for Fine-Grained Recognition
175 cvpr-2013-First-Person Activity Recognition: What Are They Doing to Me?
176 cvpr-2013-Five Shades of Grey for Fast and Reliable Camera Pose Estimation
177 cvpr-2013-FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps
178 cvpr-2013-From Local Similarity to Global Coding: An Application to Image Classification
179 cvpr-2013-From N to N+1: Multiclass Transfer Incremental Learning
180 cvpr-2013-Fully-Connected CRFs with Non-Parametric Pairwise Potential
181 cvpr-2013-Fusing Depth from Defocus and Stereo with Coded Apertures
183 cvpr-2013-GRASP Recurring Patterns from a Single View
184 cvpr-2013-Gauging Association Patterns of Chromosome Territories via Chromatic Median
185 cvpr-2013-Generalized Domain-Adaptive Dictionaries
186 cvpr-2013-GeoF: Geodesic Forests for Learning Coupled Predictors
187 cvpr-2013-Geometric Context from Videos
188 cvpr-2013-Globally Consistent Multi-label Assignment on the Ray Space of 4D Light Fields
189 cvpr-2013-Graph-Based Discriminative Learning for Location Recognition
190 cvpr-2013-Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation
191 cvpr-2013-Graph-Laplacian PCA: Closed-Form Solution and Robustness
192 cvpr-2013-Graph Matching with Anchor Nodes: A Learning Approach
194 cvpr-2013-Groupwise Registration via Graph Shrinkage on the Image Manifold
195 cvpr-2013-HDR Deghosting: How to Deal with Saturation?
196 cvpr-2013-HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences
197 cvpr-2013-Hallucinated Humans as the Hidden Context for Labeling 3D Scenes
198 cvpr-2013-Handling Noise in Single Image Deblurring Using Directional Filters
200 cvpr-2013-Harvesting Mid-level Visual Concepts from Large-Scale Internet Images
201 cvpr-2013-Heterogeneous Visual Features Fusion via Sparse Multimodal Machine
202 cvpr-2013-Hierarchical Saliency Detection
203 cvpr-2013-Hierarchical Video Representation with Trajectory Binary Partition Tree
204 cvpr-2013-Histograms of Sparse Codes for Object Detection
205 cvpr-2013-Hollywood 3D: Recognizing Actions in 3D Natural Scenes
206 cvpr-2013-Human Pose Estimation Using Body Parts Dependent Joint Regressors
207 cvpr-2013-Human Pose Estimation Using a Joint Pixel-wise and Part-wise Formulation
208 cvpr-2013-Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration
209 cvpr-2013-Hypergraphs for Joint Multi-view Reconstruction and Multi-object Tracking
210 cvpr-2013-Illumination Estimation Based on Bilayer Sparse Coding
211 cvpr-2013-Image Matting with Local and Nonlocal Smooth Priors
212 cvpr-2013-Image Segmentation by Cascaded Region Agglomeration
213 cvpr-2013-Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions
215 cvpr-2013-Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
216 cvpr-2013-Improving Image Matting Using Comprehensive Sampling Sets
217 cvpr-2013-Improving an Object Detector and Extracting Regions Using Superpixels
218 cvpr-2013-Improving the Visual Comprehension of Point Sets
219 cvpr-2013-In Defense of 3D-Label Stereo
220 cvpr-2013-In Defense of Sparsity Based Face Recognition
223 cvpr-2013-Inductive Hashing on Manifolds
224 cvpr-2013-Information Consensus for Distributed Multi-target Tracking
225 cvpr-2013-Integrating Grammar and Segmentation for Human Pose Estimation
226 cvpr-2013-Intrinsic Characterization of Dynamic Surfaces
227 cvpr-2013-Intrinsic Scene Properties from a Single RGB-D Image
228 cvpr-2013-Is There a Procedural Logic to Architecture?
229 cvpr-2013-It's Not Polite to Point: Describing People with Uncertain Attributes
230 cvpr-2013-Joint 3D Scene Reconstruction and Class Segmentation
231 cvpr-2013-Joint Detection, Tracking and Mapping by Semantic Bundle Adjustment
232 cvpr-2013-Joint Geodesic Upsampling of Depth Images
233 cvpr-2013-Joint Sparsity-Based Representation and Analysis of Unconstrained Activities
234 cvpr-2013-Joint Spectral Correspondence for Disparate Image Matching
237 cvpr-2013-Kernel Learning for Extrinsic Classification of Manifold Features
238 cvpr-2013-Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
239 cvpr-2013-Kernel Null Space Methods for Novelty Detection
240 cvpr-2013-Keypoints from Symmetries by Wave Propagation
241 cvpr-2013-Label-Embedding for Attribute-Based Classification
242 cvpr-2013-Label Propagation from ImageNet to 3D Point Clouds
243 cvpr-2013-Large-Scale Video Summarization Using Web-Image Priors
244 cvpr-2013-Large Displacement Optical Flow from Nearest Neighbor Fields
245 cvpr-2013-Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras
246 cvpr-2013-Learning Binary Codes for High-Dimensional Data Using Bilinear Projections
247 cvpr-2013-Learning Class-to-Image Distance with Object Matchings
248 cvpr-2013-Learning Collections of Part Models for Object Recognition
249 cvpr-2013-Learning Compact Binary Codes for Visual Tracking
250 cvpr-2013-Learning Cross-Domain Information Transfer for Location Recognition and Clustering
252 cvpr-2013-Learning Locally-Adaptive Decision Functions for Person Verification
253 cvpr-2013-Learning Multiple Non-linear Sub-spaces Using K-RBMs
254 cvpr-2013-Learning SURF Cascade for Fast and Accurate Object Detection
255 cvpr-2013-Learning Separable Filters
256 cvpr-2013-Learning Structured Hough Voting for Joint Object Detection and Occlusion Reasoning
257 cvpr-2013-Learning Structured Low-Rank Representations for Image Classification
258 cvpr-2013-Learning Video Saliency from Human Gaze Using Candidate Selection
259 cvpr-2013-Learning a Manifold as an Atlas
260 cvpr-2013-Learning and Calibrating Per-Location Classifiers for Visual Place Recognition
261 cvpr-2013-Learning by Associating Ambiguously Labeled Images
262 cvpr-2013-Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets
263 cvpr-2013-Learning the Change for Automatic Image Cropping
264 cvpr-2013-Learning to Detect Partially Overlapping Instances
265 cvpr-2013-Learning to Estimate and Remove Non-uniform Image Blur
266 cvpr-2013-Learning without Human Scores for Blind Image Quality Assessment
267 cvpr-2013-Least Soft-Threshold Squares Tracking
269 cvpr-2013-Light Field Distortion Feature for Transparent Object Recognition
270 cvpr-2013-Local Fisher Discriminant Analysis for Pedestrian Re-identification
271 cvpr-2013-Locally Aligned Feature Transforms across Views
272 cvpr-2013-Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery
273 cvpr-2013-Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection
274 cvpr-2013-Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization
275 cvpr-2013-Lp-Norm IDF for Large Scale Image Search
276 cvpr-2013-MKPLS: Manifold Kernel Partial Least Squares for Lipreading and Speaker Identification
277 cvpr-2013-MODEC: Multimodal Decomposable Models for Human Pose Estimation
278 cvpr-2013-Manhattan Junction Catalogue for Spatial Reasoning of Indoor Scenes
279 cvpr-2013-Manhattan Scene Understanding via XSlit Imaging
280 cvpr-2013-Maximum Cohesive Grid of Superpixels for Fast Object Localization
281 cvpr-2013-Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation
282 cvpr-2013-Measuring Crowd Collectiveness
283 cvpr-2013-Megastereo: Constructing High-Resolution Stereo Panoramas
284 cvpr-2013-Mesh Based Semantic Modelling for Indoor and Outdoor Scenes
285 cvpr-2013-Minimum Uncertainty Gap for Robust Visual Tracking
286 cvpr-2013-Mirror Surface Reconstruction from a Single Image
287 cvpr-2013-Modeling Actions through State Changes
288 cvpr-2013-Modeling Mutual Visibility Relationship in Pedestrian Detection
290 cvpr-2013-Motion Estimation for Self-Driving Cars with a Generalized Camera
291 cvpr-2013-Motionlets: Mid-level 3D Parts for Human Motion Recognition
292 cvpr-2013-Multi-agent Event Detection: Localization and Role Assignment
293 cvpr-2013-Multi-attribute Queries: To Merge or Not to Merge?
294 cvpr-2013-Multi-class Video Co-segmentation with a Generative Multi-video Model
295 cvpr-2013-Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior
296 cvpr-2013-Multi-level Discriminative Dictionary Learning towards Hierarchical Visual Categorization
298 cvpr-2013-Multi-scale Curve Detection on Surfaces
299 cvpr-2013-Multi-source Multi-scale Counting in Extremely Dense Crowd Images
300 cvpr-2013-Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow
301 cvpr-2013-Multi-target Tracking by Rank-1 Tensor Approximation
302 cvpr-2013-Multi-task Sparse Learning with Beta Process Prior for Action Recognition
303 cvpr-2013-Multi-view Photometric Stereo with Spatially Varying Isotropic Materials
304 cvpr-2013-Multipath Sparse Coding Using Hierarchical Matching Pursuit
305 cvpr-2013-Non-parametric Filtering for Geometric Detail Extraction and Material Representation
306 cvpr-2013-Non-rigid Structure from Motion with Diffusion Maps Prior
307 cvpr-2013-Non-uniform Motion Deblurring for Bilayer Scenes
308 cvpr-2013-Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques
309 cvpr-2013-Nonparametric Scene Parsing with Adaptive Feature Relevance and Semantic Context
310 cvpr-2013-Object-Centric Anomaly Detection by Attribute-Based Reasoning
311 cvpr-2013-Occlusion Patterns for Object Class Detection
313 cvpr-2013-Online Dominant and Anomalous Behavior Detection in Videos
314 cvpr-2013-Online Object Tracking: A Benchmark
315 cvpr-2013-Online Robust Dictionary Learning
316 cvpr-2013-Optical Flow Estimation Using Laplacian Mesh Energy
317 cvpr-2013-Optimal Geometric Fitting under the Truncated L2-Norm
318 cvpr-2013-Optimized Pedestrian Detection for Multiple and Occluded People
319 cvpr-2013-Optimized Product Quantization for Approximate Nearest Neighbor Search
320 cvpr-2013-Optimizing 1-Nearest Prototype Classifiers
321 cvpr-2013-PDM-ENLOR: Learning Ensemble of Local PDM-Based Regressions
324 cvpr-2013-Part-Based Visual Tracking with Online Latent Structural Learning
325 cvpr-2013-Part Discovery from Partial Correspondence
327 cvpr-2013-Pattern-Driven Colorization of 3D Surfaces
328 cvpr-2013-Pedestrian Detection with Unsupervised Multi-stage Feature Learning
329 cvpr-2013-Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
330 cvpr-2013-Photometric Ambient Occlusion
331 cvpr-2013-Physically Plausible 3D Scene Tracking: The Single Actor Hypothesis
332 cvpr-2013-Pixel-Level Hand Detection in Ego-centric Videos
333 cvpr-2013-Plane-Based Content Preserving Warps for Video Stabilization
334 cvpr-2013-Pose from Flow and Flow from Pose
335 cvpr-2013-Poselet Conditioned Pictorial Structures
336 cvpr-2013-Poselet Key-Framing: A Model for Human Activity Recognition
337 cvpr-2013-Principal Observation Ray Calibration for Tiled-Lens-Array Integral Imaging Display
338 cvpr-2013-Probabilistic Elastic Matching for Pose Variant Face Verification
340 cvpr-2013-Probabilistic Label Trees for Efficient Large Scale Image Classification
341 cvpr-2013-Procrustean Normal Distribution for Non-rigid Structure from Motion
342 cvpr-2013-Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso
343 cvpr-2013-Query Adaptive Similarity for Large Scale Object Retrieval
344 cvpr-2013-Radial Distortion Self-Calibration
346 cvpr-2013-Real-Time No-Reference Image Quality Assessment Based on Filter Learning
347 cvpr-2013-Recognize Human Activities from Partially Observed Videos
348 cvpr-2013-Recognizing Activities via Bag of Words for Attribute Dynamics
349 cvpr-2013-Reconstructing Gas Flows Using Light-Path Approximation
350 cvpr-2013-Reconstructing Loopy Curvilinear Structures Using Integer Programming
351 cvpr-2013-Recovering Line-Networks in Images by Junction-Point Processes
352 cvpr-2013-Recovering Stereo Pairs from Anaglyphs
353 cvpr-2013-Relative Hidden Markov Models for Evaluating Motion Skill
354 cvpr-2013-Relative Volume Constraints for Single View 3D Reconstruction
355 cvpr-2013-Representing Videos Using Mid-level Discriminative Patches
356 cvpr-2013-Representing and Discovering Adversarial Team Behaviors Using Player Roles
357 cvpr-2013-Revisiting Depth Layers from Occlusions
358 cvpr-2013-Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences
359 cvpr-2013-Robust Discriminative Response Map Fitting with Constrained Local Models
360 cvpr-2013-Robust Estimation of Nonrigid Transformation for Point Set Registration
361 cvpr-2013-Robust Feature Matching with Alternate Hough and Inverted Hough Transforms
362 cvpr-2013-Robust Monocular Epipolar Flow Estimation
363 cvpr-2013-Robust Multi-resolution Pedestrian Detection in Traffic Scenes
364 cvpr-2013-Robust Object Co-detection
365 cvpr-2013-Robust Real-Time Tracking of Multiple Objects by Volumetric Mass Densities
366 cvpr-2013-Robust Region Grouping via Internal Patch Statistics
367 cvpr-2013-Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem
368 cvpr-2013-Rolling Shutter Camera Calibration
369 cvpr-2013-Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination
370 cvpr-2013-SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning
372 cvpr-2013-SLAM++: Simultaneous Localisation and Mapping at the Level of Objects
373 cvpr-2013-SWIGS: A Swift Guided Sampling Method
374 cvpr-2013-Saliency Aggregation: A Data-Driven Approach
375 cvpr-2013-Saliency Detection via Graph-Based Manifold Ranking
376 cvpr-2013-Salient Object Detection: A Discriminative Regional Feature Integration Approach
377 cvpr-2013-Sample-Specific Late Fusion for Visual Category Recognition
378 cvpr-2013-Sampling Strategies for Real-Time Action Recognition
379 cvpr-2013-Scalable Sparse Subspace Clustering
380 cvpr-2013-Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
381 cvpr-2013-Scene Parsing by Integrating Function, Geometry and Appearance Models
382 cvpr-2013-Scene Text Recognition Using Part-Based Tree-Structured Character Detection
383 cvpr-2013-Seeking the Strongest Rigid Detector
384 cvpr-2013-Segment-Tree Based Cost Aggregation for Stereo Matching
385 cvpr-2013-Selective Transfer Machine for Personalized Facial Action Unit Detection
386 cvpr-2013-Self-Paced Learning for Long-Term Tracking
387 cvpr-2013-Semi-supervised Domain Adaptation with Instance Constraints
388 cvpr-2013-Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video
389 cvpr-2013-Semi-supervised Learning with Constraints for Person Identification in Multimedia Data
390 cvpr-2013-Semi-supervised Node Splitting for Random Forest Construction
391 cvpr-2013-Sensing and Recognizing Surface Textures Using a GelSight Sensor
392 cvpr-2013-Separable Dictionary Learning
393 cvpr-2013-Separating Signal from Noise Using Patch Recurrence across Scales
394 cvpr-2013-Shading-Based Shape Refinement of RGB-D Images
396 cvpr-2013-Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback
397 cvpr-2013-Simultaneous Super-Resolution of Depth and Images Using a Single Camera
398 cvpr-2013-Single-Pedestrian Detection Aided by Multi-pedestrian Detection
400 cvpr-2013-Single Image Calibration of Multi-axial Imaging Systems
401 cvpr-2013-Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection
402 cvpr-2013-Social Role Discovery in Human Events
403 cvpr-2013-Sparse Output Coding for Large-Scale Visual Recognition
404 cvpr-2013-Sparse Quantization for Patch Description
405 cvpr-2013-Sparse Subspace Denoising for Image Manifolds
406 cvpr-2013-Spatial Inference Machines
408 cvpr-2013-Spatiotemporal Deformable Part Models for Action Detection
409 cvpr-2013-Spectral Modeling and Relighting of Reflective-Fluorescent Scenes
410 cvpr-2013-Specular Reflection Separation Using Dark Channel Prior
411 cvpr-2013-Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
412 cvpr-2013-Stochastic Deconvolution
413 cvpr-2013-Story-Driven Summarization for Egocentric Video
414 cvpr-2013-Structure Preserving Object Tracking
415 cvpr-2013-Structured Face Hallucination
416 cvpr-2013-Studying Relationships between Human Gaze, Description, and Computer Vision
417 cvpr-2013-Subcategory-Aware Object Classification
418 cvpr-2013-Submodular Salient Region Detection
419 cvpr-2013-Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation
420 cvpr-2013-Supervised Descent Method and Its Applications to Face Alignment
421 cvpr-2013-Supervised Kernel Descriptors for Visual Recognition
422 cvpr-2013-Tag Taxonomy Aware Dictionary Learning for Region Tagging
424 cvpr-2013-Templateless Quasi-rigid Shape Modeling with Implicit Loop-Closure
425 cvpr-2013-Tensor-Based High-Order Semantic Relation Transfer for Semantic Scene Segmentation
426 cvpr-2013-Tensor-Based Human Body Modeling
427 cvpr-2013-Texture Enhanced Image Denoising via Gradient Histogram Preservation
428 cvpr-2013-The Episolar Constraint: Monocular Shape from Shadow Correspondence
429 cvpr-2013-The Generalized Laplacian Distance and Its Applications for Visual Matching
430 cvpr-2013-The SVM-Minus Similarity Score for Video Face Recognition
431 cvpr-2013-The Variational Structure of Disparity and Regularization of 4D Light Fields
432 cvpr-2013-Three-Dimensional Bilateral Symmetry Plane Estimation in the Phase Domain
434 cvpr-2013-Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior
435 cvpr-2013-Towards Contactless, Low-Cost and Accurate 3D Fingerprint Identification
437 cvpr-2013-Towards Fast and Accurate Segmentation
438 cvpr-2013-Towards Pose Robust Face Recognition
439 cvpr-2013-Tracking Human Pose by Tracking Symmetric Parts
440 cvpr-2013-Tracking People and Their Objects
441 cvpr-2013-Tracking Sports Players with Context-Conditioned Motion Models
442 cvpr-2013-Transfer Sparse Coding for Robust Image Representation
443 cvpr-2013-Uncalibrated Photometric Stereo for Unknown Isotropic Reflectances
445 cvpr-2013-Understanding Bayesian Rooms Using Composite 3D Object Models
446 cvpr-2013-Understanding Indoor Scenes Using 3D Geometric Phrases
447 cvpr-2013-Underwater Camera Calibration Using Wavelength Triangulation
448 cvpr-2013-Universality of the Local Marginal Polytope
449 cvpr-2013-Unnatural L0 Sparse Representation for Natural Image Deblurring
450 cvpr-2013-Unsupervised Joint Object Discovery and Segmentation in Internet Images
451 cvpr-2013-Unsupervised Salience Learning for Person Re-identification
452 cvpr-2013-Vantage Feature Frames for Fine-Grained Categorization
453 cvpr-2013-Video Editing with Temporal, Spatial and Appearance Consistency
456 cvpr-2013-Visual Place Recognition with Repetitive Structures
457 cvpr-2013-Visual Tracking via Locality Sensitive Histograms
458 cvpr-2013-Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds
459 cvpr-2013-Watching Unlabeled Video Helps Learn New Human Actions from Very Few Labeled Snapshots
460 cvpr-2013-Weakly-Supervised Dual Clustering for Image Semantic Segmentation
461 cvpr-2013-Weakly Supervised Learning for Attribute Localization in Outdoor Scenes
463 cvpr-2013-What's in a Name? First Names as Facial Attributes
464 cvpr-2013-What Makes a Patch Distinct?
465 cvpr-2013-What Object Motion Reveals about Shape with Unknown BRDF and Lighting
466 cvpr-2013-Whitened Expectation Propagation: Non-Lambertian Shape from Shading and Shadow
467 cvpr-2013-Wide-Baseline Hair Capture Using Strand-Based Refinement
468 cvpr-2013-Winding Number for Region-Boundary Consistent Salient Contour Extraction