iccv iccv2013 knowledge-graph by maker-knowledge-mining
1 iccv-2013-3DNN: Viewpoint Invariant 3D Geometry Matching for Scene Understanding
Author: Scott Satkin, Martial Hebert
Abstract: We present a new algorithm 3DNN (3D NearestNeighbor), which is capable of matching an image with 3D data, independently of the viewpoint from which the image was captured. By leveraging rich annotations associated with each image, our algorithm can automatically produce precise and detailed 3D models of a scene from a single image. Moreover, we can transfer information across images to accurately label and segment objects in a scene. The true benefit of 3DNN compared to a traditional 2D nearest-neighbor approach is that by generalizing across viewpoints, we free ourselves from the need to have training examples captured from all possible viewpoints. Thus, we are able to achieve comparable results using orders of magnitude less data, and recognize objects from never-beforeseen viewpoints. In this work, we describe the 3DNN algorithm and rigorously evaluate its performance for the tasks of geometry estimation and object detection/segmentation. By decoupling the viewpoint and the geometry of an image, we develop a scene matching approach which is truly 100% viewpoint invariant, yielding state-of-the-art performance on challenging data.
2 iccv-2013-3D Scene Understanding by Voxel-CRF
Author: Byung-Soo Kim, Pushmeet Kohli, Silvio Savarese
Abstract: Scene understanding is an important yet very challenging problem in computer vision. In the past few years, researchers have taken advantage of the recent diffusion of depth-RGB (RGB-D) cameras to help simplify the problem of inferring scene semantics. However, while the added 3D geometry is certainly useful to segment out objects with different depth values, it also adds complications in that the 3D geometry is often incorrect because of noisy depth measurements and the actual 3D extent of the objects is usually unknown because of occlusions. In this paper we propose a new method that allows us to jointly refine the 3D reconstruction of the scene (raw depth values) while accurately segmenting out the objects or scene elements from the 3D reconstruction. This is achieved by introducing a new model which we called Voxel-CRF. The Voxel-CRF model is based on the idea of constructing a conditional random field over a 3D volume of interest which captures the semantic and 3D geometric relationships among different elements (voxels) of the scene. Such model allows to jointly estimate (1) a dense voxel-based 3D reconstruction and (2) the semantic labels associated with each voxel even in presence of par- tial occlusions using an approximate yet efficient inference strategy. We evaluated our method on the challenging NYU Depth dataset (Version 1and 2). Experimental results show that our method achieves competitive accuracy in inferring scene semantics and visually appealing results in improving the quality of the 3D reconstruction. We also demonstrate an interesting application of object removal and scene completion from RGB-D images.
3 iccv-2013-3D Sub-query Expansion for Improving Sketch-Based Multi-view Image Retrieval
Author: Yen-Liang Lin, Cheng-Yu Huang, Hao-Jeng Wang, Winston Hsu
Abstract: We propose a 3D sub-query expansion approach for boosting sketch-based multi-view image retrieval. The core idea of our method is to automatically convert two (guided) 2D sketches into an approximated 3D sketch model, and then generate multi-view sketches as expanded sub-queries to improve the retrieval performance. To learn the weights among synthesized views (sub-queries), we present a new multi-query feature to model the similarity between subqueries and dataset images, and formulate it into a convex optimization problem. Our approach shows superior performance compared with the state-of-the-art approach on a public multi-view image dataset. Moreover, we also conduct sensitivity tests to analyze the parameters of our approach based on the gathered user sketches.
4 iccv-2013-ACTIVE: Activity Concept Transitions in Video Event Classification
Author: Chen Sun, Ram Nevatia
Abstract: The goal of high level event classification from videos is to assign a single, high level event label to each query video. Traditional approaches represent each video as a set of low level features and encode it into a fixed length feature vector (e.g. Bag-of-Words), which leave a big gap between low level visual features and high level events. Our paper tries to address this problem by exploiting activity concept transitions in video events (ACTIVE). A video is treated as a sequence of short clips, all of which are observations corresponding to latent activity concept variables in a Hidden Markov Model (HMM). We propose to apply Fisher Kernel techniques so that the concept transitions over time can be encoded into a compact and fixed length feature vector very efficiently. Our approach can utilize concept annotations from independent datasets, and works well even with a very small number of training samples. Experiments on the challenging NIST TRECVID Multimedia Event Detection (MED) dataset shows our approach performs favorably over the state-of-the-art.
5 iccv-2013-A Color Constancy Model with Double-Opponency Mechanisms
Author: Shaobing Gao, Kaifu Yang, Chaoyi Li, Yongjie Li
Abstract: The double-opponent color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. We introduce a new color constancy model by imitating the functional properties of the HVS from the retina to the double-opponent cells in V1. The idea behind the model originates from the observation that the color distribution of the responses of double-opponent cells to the input color-biased images coincides well with the light source direction. Then the true illuminant color of a scene is easily estimated by searching for the maxima of the separate RGB channels of the responses of double-opponent cells in the RGB space. Our systematical experimental evaluations on two commonly used image datasets show that the proposed model can produce competitive results in comparison to the complex state-of-the-art approaches, but with a simple implementation and without the need for training.
6 iccv-2013-A Convex Optimization Framework for Active Learning
Author: Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, S. Shankar Sasrty
Abstract: In many image/video/web classification problems, we have access to a large number of unlabeled samples. However, it is typically expensive and time consuming to obtain labels for the samples. Active learning is the problem of progressively selecting and annotating the most informative unlabeled samples, in order to obtain a high classification performance. Most existing active learning algorithms select only one sample at a time prior to retraining the classifier. Hence, they are computationally expensive and cannot take advantage of parallel labeling systems such as Mechanical Turk. On the other hand, algorithms that allow the selection of multiple samples prior to retraining the classifier, may select samples that have significant information overlap or they involve solving a non-convex optimization. More importantly, the majority of active learning algorithms are developed for a certain classifier type such as SVM. In this paper, we develop an efficient active learning framework based on convex programming, which can select multiple samples at a time for annotation. Unlike the state of the art, our algorithm can be used in conjunction with any type of classifiers, including those of the fam- ily of the recently proposed Sparse Representation-based Classification (SRC). We use the two principles of classifier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informative unlabeled samples, which have the least information overlap. Our method can incorporate the data distribution in the selection process by using the appropriate dissimilarity between pairs of samples. We show the effectiveness of our framework in person detection, scene categorization and face recognition on real-world datasets.
7 iccv-2013-A Deep Sum-Product Architecture for Robust Facial Attributes Analysis
Author: Ping Luo, Xiaogang Wang, Xiaoou Tang
Abstract: Recent works have shown that facial attributes are useful in a number of applications such as face recognition and retrieval. However, estimating attributes in images with large variations remains a big challenge. This challenge is addressed in this paper. Unlike existing methods that assume the independence of attributes during their estimation, our approach captures the interdependencies of local regions for each attribute, as well as the high-order correlations between different attributes, which makes it more robust to occlusions and misdetection of face regions. First, we have modeled region interdependencies with a discriminative decision tree, where each node consists of a detector and a classifier trained on a local region. The detector allows us to locate the region, while the classifier determines the presence or absence of an attribute. Second, correlations of attributes and attribute predictors are modeled by organizing all of the decision trees into a large sum-product network (SPN), which is learned by the EM algorithm and yields the most probable explanation (MPE) of the facial attributes in terms of the region ’s localization and classification. Experimental results on a large data set with 22, 400 images show the effectiveness of the proposed approach.
8 iccv-2013-A Deformable Mixture Parsing Model with Parselets
Author: Jian Dong, Qiang Chen, Wei Xia, Zhongyang Huang, Shuicheng Yan
Abstract: In this work, we address the problem of human parsing, namely partitioning the human body into semantic regions, by using the novel Parselet representation. Previous works often consider solving the problem of human pose estimation as the prerequisite of human parsing. We argue that these approaches cannot obtain optimal pixel level parsing due to the inconsistent targets between these tasks. In this paper, we propose to use Parselets as the building blocks of our parsing model. Parselets are a group of parsable segments which can generally be obtained by lowlevel over-segmentation algorithms and bear strong semantic meaning. We then build a Deformable Mixture Parsing Model (DMPM) for human parsing to simultaneously handle the deformation and multi-modalities of Parselets. The proposed model has two unique characteristics: (1) the possible numerous modalities of Parselet ensembles are exhibited as the “And-Or” structure of sub-trees; (2) to further solve the practical problem of Parselet occlusion or absence, we directly model the visibility property at some leaf nodes. The DMPM thus directly solves the problem of human parsing by searching for the best graph configura- tionfrom apool ofParselet hypotheses without intermediate tasks. Comprehensive evaluations demonstrate the encouraging performance of the proposed approach.
9 iccv-2013-A Flexible Scene Representation for 3D Reconstruction Using an RGB-D Camera
Author: Diego Thomas, Akihiro Sugimoto
Abstract: Updating a global 3D model with live RGB-D measurements has proven to be successful for 3D reconstruction of indoor scenes. Recently, a Truncated Signed Distance Function (TSDF) volumetric model and a fusion algorithm have been introduced (KinectFusion), showing significant advantages such as computational speed and accuracy of the reconstructed scene. This algorithm, however, is expensive in memory when constructing and updating the global model. As a consequence, the method is not well scalable to large scenes. We propose a new flexible 3D scene representation using a set of planes that is cheap in memory use and, nevertheless, achieves accurate reconstruction of indoor scenes from RGB-D image sequences. Projecting the scene onto different planes reduces significantly the size of the scene representation and thus it allows us to generate a global textured 3D model with lower memory requirement while keeping accuracy and easiness to update with live RGB-D measurements. Experimental results demonstrate that our proposed flexible 3D scene representation achieves accurate reconstruction, while keeping the scalability for large indoor scenes.
10 iccv-2013-A Framework for Shape Analysis via Hilbert Space Embedding
Author: Sadeep Jayasumana, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi
Abstract: We propose a framework for 2D shape analysis using positive definite kernels defined on Kendall’s shape manifold. Different representations of 2D shapes are known to generate different nonlinear spaces. Due to the nonlinearity of these spaces, most existing shape classification algorithms resort to nearest neighbor methods and to learning distances on shape spaces. Here, we propose to map shapes on Kendall’s shape manifold to a high dimensional Hilbert space where Euclidean geometry applies. To this end, we introduce a kernel on this manifold that permits such a mapping, and prove its positive definiteness. This kernel lets us extend kernel-based algorithms developed for Euclidean spaces, such as SVM, MKL and kernel PCA, to the shape manifold. We demonstrate the benefits of our approach over the state-of-the-art methods on shape classification, clustering and retrieval.
11 iccv-2013-A Fully Hierarchical Approach for Finding Correspondences in Non-rigid Shapes
Author: Ivan Sipiran, Benjamin Bustos
Abstract: This paper presents a hierarchical method for finding correspondences in non-rigid shapes. We propose a new representation for 3D meshes: the decomposition tree. This structure characterizes the recursive decomposition process of a mesh into regions of interest and keypoints. The internal nodes contain regions of interest (which may be recursively decomposed) and the leaf nodes contain the keypoints to be matched. We also propose a hierarchical matching algorithm that performs in a level-wise manner. The matching process is guided by the similarity between regions in high levels of the tree, until reaching the keypoints stored in the leaves. This allows us to reduce the search space of correspondences, making also the matching process efficient. We evaluate the effectiveness of our approach using the SHREC’2010 robust correspondence benchmark. In addition, we show that our results outperform the state of the art.
12 iccv-2013-A General Dense Image Matching Framework Combining Direct and Feature-Based Costs
Author: Jim Braux-Zin, Romain Dupont, Adrien Bartoli
Abstract: Dense motion field estimation (typically Romain Dupont1 romain . dupont @ cea . fr Adrien Bartoli2 adrien . bart o l @ gmai l com i . 2 ISIT, Universit e´ d’Auvergne/CNRS, France sions are explicitly modeled [32, 13]. Coarse-to-fine warping improves global convergence by making the assumption that optical flow, the motion of smaller structures is similar to the motion of stereo disparity and surface registration) is a key computer vision problem. Many solutions have been proposed to compute small or large displacements, narrow or wide baseline stereo disparity, but a unified methodology is still lacking. We here introduce a general framework that robustly combines direct and feature-based matching. The feature-based cost is built around a novel robust distance function that handles keypoints and “weak” features such as segments. It allows us to use putative feature matches which may contain mismatches to guide dense motion estimation out of local minima. Our framework uses a robust direct data term (AD-Census). It is implemented with a powerful second order Total Generalized Variation regularization with external and self-occlusion reasoning. Our framework achieves state of the art performance in several cases (standard optical flow benchmarks, wide-baseline stereo and non-rigid surface registration). Our framework has a modular design that customizes to specific application needs.
13 iccv-2013-A General Two-Step Approach to Learning-Based Hashing
Author: Guosheng Lin, Chunhua Shen, David Suter, Anton van_den_Hengel
Abstract: Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problemspecific hashing methods. Our framework decomposes the hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive experiments demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art.
14 iccv-2013-A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding
Author: Wangmeng Zuo, Deyu Meng, Lei Zhang, Xiangchu Feng, David Zhang
Abstract: In many sparse coding based image restoration and image classification problems, using non-convex ?p-norm minimization (0 ≤ p < 1) can often obtain better results than timhei convex 0?1 -norm m 1)ini camniza otfiteonn. Ab naiunm bbeetrt of algorithms, e.g., iteratively reweighted least squares (IRLS), iteratively thresholding method (ITM-?p), and look-up table (LUT), have been proposed for non-convex ?p-norm sparse coding, while some analytic solutions have been suggested for some specific values of p. In this paper, by extending the popular soft-thresholding operator, we propose a generalized iterated shrinkage algorithm (GISA) for ?p-norm non-convex sparse coding. Unlike the analytic solutions, the proposed GISA algorithm is easy to implement, and can be adopted for solving non-convex sparse coding problems with arbitrary p values. Compared with LUT, GISA is more general and does not need to compute and store the look-up tables. Compared with IRLS and ITM-?p, GISA is theoretically more solid and can achieve more accurate solutions. Experiments on image restoration and sparse coding based face recognition are conducted to validate the performance of GISA. ××
15 iccv-2013-A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks
Author: Yi-Lei Chen, Chiou-Ting Hsu
Abstract: In this paper, we propose a novel low-rank appearance model for removing rain streaks. Different from previous work, our method needs neither rain pixel detection nor time-consuming dictionary learning stage. Instead, as rain streaks usually reveal similar and repeated patterns on imaging scene, we propose and generalize a low-rank model from matrix to tensor structure in order to capture the spatio-temporally correlated rain streaks. With the appearance model, we thus remove rain streaks from image/video (and also other high-order image structure) in a unified way. Our experimental results demonstrate competitive (or even better) visual quality and efficient run-time in comparison with state of the art.
Author: Yun Zeng, Chaohui Wang, Xianfeng Gu, Dimitris Samaras, Nikos Paragios
Abstract: We propose a novel approach for dense non-rigid 3D surface registration, which brings together Riemannian geometry and graphical models. To this end, we first introduce a generic deformation model, called Canonical Distortion Coefficients (CDCs), by characterizing the deformation of every point on a surface using the distortions along its two principle directions. This model subsumes the deformation groups commonly used in surface registration such as isometry and conformality, and is able to handle more complex deformations. We also derive its discrete counterpart which can be computed very efficiently in a closed form. Based on these, we introduce a higher-order Markov Random Field (MRF) model which seamlessly integrates our deformation model and a geometry/texture similarity metric. Then we jointly establish the optimal correspondences for all the points via maximum a posteriori (MAP) inference. Moreover, we develop a parallel optimization algorithm to efficiently perform the inference for the proposed higher-order MRF model. The resulting registration algorithm outperforms state-of-the-art methods in both dense non-rigid 3D surface registration and tracking.
17 iccv-2013-A Global Linear Method for Camera Pose Registration
Author: Nianjuan Jiang, Zhaopeng Cui, Ping Tan
Abstract: We present a linear method for global camera pose registration from pairwise relative poses encoded in essential matrices. Our method minimizes an approximate geometric error to enforce the triangular relationship in camera triplets. This formulation does not suffer from the typical ‘unbalanced scale ’ problem in linear methods relying on pairwise translation direction constraints, i.e. an algebraic error; nor the system degeneracy from collinear motion. In the case of three cameras, our method provides a good linear approximation of the trifocal tensor. It can be directly scaled up to register multiple cameras. The results obtained are accurate for point triangulation and can serve as a good initialization for final bundle adjustment. We evaluate the algorithm performance with different types of data and demonstrate its effectiveness. Our system produces good accuracy, robustness, and outperforms some well-known systems on efficiency.
18 iccv-2013-A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution
Author: Martin Kiechle, Simon Hawe, Martin Kleinsteuber
Abstract: High-resolution depth maps can be inferred from lowresolution depth measurements and an additional highresolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity . go l e i um . de . .t ities together with the knowledge of the relative positions between all views. Despite very active research in this area and significant improvements over the past years, stereo methods still struggle with noise, texture-less regions, repetitive texture, and occluded areas. For an overview of stereo methods, the reader is referred to [25]. and depth information. This model is based on the assumption that the co-supports of corresponding bimodal image structures are aligned when computed by a suitable pair of analysis operators. No analytic form of such operators ex- ist and we propose a method for learning them from a set of registered training signals. This learning process is done offline and returns a bimodal analysis operator that is universally applicable to natural scenes. We use this to exploit the bimodal co-sparse analysis model as a prior for solving inverse problems, which leads to an efficient algorithm for depth map super-resolution.
19 iccv-2013-A Learning-Based Approach to Reduce JPEG Artifacts in Image Matting
Author: Inchang Choi, Sunyeong Kim, Michael S. Brown, Yu-Wing Tai
Abstract: Single image matting techniques assume high-quality input images. The vast majority of images on the web and in personal photo collections are encoded using JPEG compression. JPEG images exhibit quantization artifacts that adversely affect the performance of matting algorithms. To address this situation, we propose a learning-based post-processing method to improve the alpha mattes extracted from JPEG images. Our approach learns a set of sparse dictionaries from training examples that are used to transfer details from high-quality alpha mattes to alpha mattes corrupted by JPEG compression. Three different dictionaries are defined to accommodate different object structure (long hair, short hair, and sharp boundaries). A back-projection criteria combined within an MRF framework is used to automatically select the best dictionary to apply on the object’s local boundary. We demonstrate that our method can produces superior results over existing state-of-the-art matting algorithms on a variety of inputs and compression levels.
20 iccv-2013-A Max-Margin Perspective on Sparse Representation-Based Classification
Author: Zhaowen Wang, Jianchao Yang, Nasser Nasrabadi, Thomas Huang
Abstract: Sparse Representation-based Classification (SRC) is a powerful tool in distinguishing signal categories which lie on different subspaces. Despite its wide application to visual recognition tasks, current understanding of SRC is solely based on a reconstructive perspective, which neither offers any guarantee on its classification performance nor provides any insight on how to design a discriminative dictionary for SRC. In this paper, we present a novel perspective towards SRC and interpret it as a margin classifier. The decision boundary and margin of SRC are analyzed in local regions where the support of sparse code is stable. Based on the derived margin, we propose a hinge loss function as the gauge for the classification performance of SRC. A stochastic gradient descent algorithm is implemented to maximize the margin of SRC and obtain more discriminative dictionaries. Experiments validate the effectiveness of the proposed approach in predicting classification performance and improving dictionary quality over reconstructive ones. Classification results competitive with other state-ofthe-art sparse coding methods are reported on several data sets.
21 iccv-2013-A Method of Perceptual-Based Shape Decomposition
22 iccv-2013-A New Adaptive Segmental Matching Measure for Human Activity Recognition
23 iccv-2013-A New Image Quality Metric for Image Auto-denoising
24 iccv-2013-A Non-parametric Bayesian Network Prior of Human Pose
25 iccv-2013-A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models
26 iccv-2013-A Practical Transfer Learning Algorithm for Face Verification
28 iccv-2013-A Rotational Stereo Model Based on XSlit Imaging
30 iccv-2013-A Simple Model for Intrinsic Image Decomposition with Depth Cues
31 iccv-2013-A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects
32 iccv-2013-A Unified Rolling Shutter and Motion Blur Model for 3D Visual Registration
33 iccv-2013-A Unified Video Segmentation Benchmark: Annotation, Metrics and Analysis
34 iccv-2013-Abnormal Event Detection at 150 FPS in MATLAB
35 iccv-2013-Accurate Blur Models vs. Image Priors in Single Image Super-resolution
36 iccv-2013-Accurate and Robust 3D Facial Capture Using a Single RGBD Camera
37 iccv-2013-Action Recognition and Localization by Hierarchical Space-Time Segments
38 iccv-2013-Action Recognition with Actons
39 iccv-2013-Action Recognition with Improved Trajectories
40 iccv-2013-Action and Event Recognition with Fisher Vectors on a Compact Feature Set
41 iccv-2013-Active Learning of an Action Detector from Untrimmed Videos
42 iccv-2013-Active MAP Inference in CRFs for Efficient Semantic Segmentation
43 iccv-2013-Active Visual Recognition with Expertise Estimation in Crowdsourcing
44 iccv-2013-Adapting Classification Cascades to New Domains
45 iccv-2013-Affine-Constrained Group Sparse Coding and Its Application to Image-Based Classifications
46 iccv-2013-Allocentric Pose Estimation
47 iccv-2013-Alternating Regression Forests for Object Detection and Pose Estimation
48 iccv-2013-An Adaptive Descriptor Design for Object Recognition in the Wild
50 iccv-2013-Analysis of Scores, Datasets, and Models in Visual Saliency Prediction
51 iccv-2013-Anchored Neighborhood Regression for Fast Example-Based Super-Resolution
52 iccv-2013-Attribute Adaptation for Personalized Image Search
53 iccv-2013-Attribute Dominance: What Pops Out?
54 iccv-2013-Attribute Pivots for Guiding Relevance Feedback in Image Search
55 iccv-2013-Automatic Kronecker Product Model Based Detection of Repeated Patterns in 2D Urban Images
56 iccv-2013-Automatic Registration of RGB-D Scans via Salient Directions
57 iccv-2013-BOLD Features to Detect Texture-less Objects
58 iccv-2013-Bayesian 3D Tracking from Monocular Video
59 iccv-2013-Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
60 iccv-2013-Bayesian Robust Matrix Factorization for Image and Video Processing
61 iccv-2013-Beyond Hard Negative Mining: Efficient Detector Learning via Block-Circulant Decomposition
64 iccv-2013-Box in the Box: Joint 3D Layout and Object Reasoning from Single Images
66 iccv-2013-Building Part-Based Object Detectors via 3D Geometry
67 iccv-2013-Calibration-Free Gaze Estimation Using Human Gaze Patterns
68 iccv-2013-Camera Alignment Using Trajectory Intersections in Unsynchronized Videos
69 iccv-2013-Capturing Global Semantic Relationships for Facial Action Unit Recognition
70 iccv-2013-Cascaded Shape Space Pruning for Robust Facial Landmark Detection
71 iccv-2013-Category-Independent Object-Level Saliency Detection
72 iccv-2013-Characterizing Layouts of Outdoor Scenes Using Spatial Topic Processes
73 iccv-2013-Class-Specific Simplex-Latent Dirichlet Allocation for Image Classification
74 iccv-2013-Co-segmentation by Composition
75 iccv-2013-CoDeL: A Human Co-detection and Labeling Framework
76 iccv-2013-Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees
77 iccv-2013-Codemaps - Segment, Classify and Search Objects Locally
78 iccv-2013-Coherent Motion Segmentation in Moving Camera Videos Using Optical Flow Orientations
79 iccv-2013-Coherent Object Detection with 3D Geometric Context from a Single Image
80 iccv-2013-Collaborative Active Learning of a Kernel Machine Ensemble for Recognition
81 iccv-2013-Combining the Right Features for Complex Event Recognition
82 iccv-2013-Compensating for Motion during Direct-Global Separation
83 iccv-2013-Complementary Projection Hashing
84 iccv-2013-Complex 3D General Object Reconstruction from Line Drawings
86 iccv-2013-Concurrent Action Detection with Structural Prediction
87 iccv-2013-Conservation Tracking
88 iccv-2013-Constant Time Weighted Median Filtering for Stereo Matching and Beyond
89 iccv-2013-Constructing Adaptive Complex Cells for Robust Visual Tracking
90 iccv-2013-Content-Aware Rotation
91 iccv-2013-Contextual Hypergraph Modeling for Salient Object Detection
92 iccv-2013-Corrected-Moment Illuminant Estimation
93 iccv-2013-Correlation Adaptive Subspace Segmentation by Trace Lasso
94 iccv-2013-Correntropy Induced L2 Graph for Robust Subspace Clustering
95 iccv-2013-Cosegmentation and Cosketch by Unsupervised Learning
97 iccv-2013-Coupling Alignments with Recognition for Still-to-Video Face Recognition
98 iccv-2013-Cross-Field Joint Image Restoration via Scale Map
99 iccv-2013-Cross-View Action Recognition over Heterogeneous Feature Spaces
100 iccv-2013-Curvature-Aware Regularization on Riemannian Submanifolds
101 iccv-2013-DCSH - Matching Patches in RGBD Images
102 iccv-2013-Data-Driven 3D Primitives for Single Image Understanding
103 iccv-2013-Deblurring by Example Using Dense Correspondence
104 iccv-2013-Decomposing Bag of Words Histograms
105 iccv-2013-DeepFlow: Large Displacement Optical Flow with Deep Matching
106 iccv-2013-Deep Learning Identity-Preserving Face Space
107 iccv-2013-Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction
108 iccv-2013-Depth from Combining Defocus and Correspondence Using Light-Field Cameras
109 iccv-2013-Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?
110 iccv-2013-Detecting Curved Symmetric Parts Using a Deformable Disc Model
111 iccv-2013-Detecting Dynamic Objects with Multi-view Background Subtraction
114 iccv-2013-Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
115 iccv-2013-Direct Optimization of Frame-to-Frame Rotation
116 iccv-2013-Directed Acyclic Graph Kernels for Action Recognition
117 iccv-2013-Discovering Details and Scene Structure with Hierarchical Iconoid Shift
118 iccv-2013-Discovering Object Functionality
119 iccv-2013-Discriminant Tracking Using Tensor Representation with Semi-supervised Improvement
121 iccv-2013-Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach
122 iccv-2013-Distributed Low-Rank Subspace Segmentation
123 iccv-2013-Domain Adaptive Classification
125 iccv-2013-Drosophila Embryo Stage Annotation Using Label Propagation
126 iccv-2013-Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
127 iccv-2013-Dynamic Pooling for Complex Event Recognition
128 iccv-2013-Dynamic Probabilistic Volumetric Models
129 iccv-2013-Dynamic Scene Deblurring
130 iccv-2013-Dynamic Structured Model Selection
132 iccv-2013-Efficient 3D Scene Labeling Using Fields of Trees
133 iccv-2013-Efficient Hand Pose Estimation from a Single Depth Image
134 iccv-2013-Efficient Higher-Order Clustering on the Grassmann Manifold
135 iccv-2013-Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
136 iccv-2013-Efficient Pedestrian Detection by Directly Optimizing the Partial Area under the ROC Curve
137 iccv-2013-Efficient Salient Region Detection with Soft Image Abstraction
138 iccv-2013-Efficient and Robust Large-Scale Rotation Averaging
139 iccv-2013-Elastic Fragments for Dense Scene Reconstruction
140 iccv-2013-Elastic Net Constraints for Shape Matching
141 iccv-2013-Enhanced Continuous Tabu Search for Parameter Estimation in Multiview Geometry
142 iccv-2013-Ensemble Projection for Semi-supervised Image Classification
143 iccv-2013-Estimating Human Pose with Flowing Puppets
144 iccv-2013-Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors
145 iccv-2013-Estimating the Material Properties of Fabric from Video
146 iccv-2013-Event Detection in Complex Scenes Using Interval Temporal Constraints
147 iccv-2013-Event Recognition in Photo Collections with a Stopwatch HMM
148 iccv-2013-Example-Based Facade Texture Synthesis
149 iccv-2013-Exemplar-Based Graph Matching for Robust Facial Landmark Localization
151 iccv-2013-Exploiting Reflection Change for Automatic Reflection Removal
152 iccv-2013-Extrinsic Camera Calibration without a Direct View Using Spherical Mirror
153 iccv-2013-Face Recognition Using Face Patch Networks
154 iccv-2013-Face Recognition via Archetype Hull Ranking
155 iccv-2013-Facial Action Unit Event Detection by Cascade of Tasks
156 iccv-2013-Fast Direct Super-Resolution by Simple Functions
157 iccv-2013-Fast Face Detector Training Using Tailored Views
158 iccv-2013-Fast High Dimensional Vector Multiplication Face Recognition
159 iccv-2013-Fast Neighborhood Graph Search Using Cartesian Concatenation
160 iccv-2013-Fast Object Segmentation in Unconstrained Video
161 iccv-2013-Fast Sparsity-Based Orthogonal Dictionary Learning for Image Restoration
162 iccv-2013-Fast Subspace Search via Grassmannian Based Hashing
163 iccv-2013-Feature Weighting via Optimal Thresholding for Video Analysis
164 iccv-2013-Fibonacci Exposure Bracketing for High Dynamic Range Imaging
165 iccv-2013-Find the Best Path: An Efficient and Accurate Classifier for Image Hierarchies
166 iccv-2013-Finding Actors and Actions in Movies
167 iccv-2013-Finding Causal Interactions in Video Sequences
169 iccv-2013-Fine-Grained Categorization by Alignments
170 iccv-2013-Fingerspelling Recognition with Semi-Markov Conditional Random Fields
171 iccv-2013-Fix Structured Learning of 2013 ICCV paper k2opt.pdf
172 iccv-2013-Flattening Supervoxel Hierarchies by the Uniform Entropy Slice
173 iccv-2013-Fluttering Pattern Generation Using Modified Legendre Sequence for Coded Exposure Imaging
174 iccv-2013-Forward Motion Deblurring
176 iccv-2013-From Large Scale Image Categorization to Entry-Level Categories
177 iccv-2013-From Point to Set: Extend the Learning of Distance Metrics
178 iccv-2013-From Semi-supervised to Transfer Counting of Crowds
179 iccv-2013-From Subcategories to Visual Composites: A Multi-level Framework for Object Detection
180 iccv-2013-From Where and How to What We See
181 iccv-2013-Frustratingly Easy NBNN Domain Adaptation
182 iccv-2013-GOSUS: Grassmannian Online Subspace Updates with Structured-Sparsity
183 iccv-2013-Geometric Registration Based on Distortion Estimation
184 iccv-2013-Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion
185 iccv-2013-Go-ICP: Solving 3D Registration Efficiently and Globally Optimally
186 iccv-2013-GrabCut in One Cut
187 iccv-2013-Group Norm for Learning Structured SVMs with Unstructured Latent Variables
189 iccv-2013-HOGgles: Visualizing Object Detection Features
190 iccv-2013-Handling Occlusions with Franken-Classifiers
191 iccv-2013-Handling Uncertain Tags in Visual Recognition
192 iccv-2013-Handwritten Word Spotting with Corrected Attributes
194 iccv-2013-Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model
195 iccv-2013-Hidden Factor Analysis for Age Invariant Face Recognition
196 iccv-2013-Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation
198 iccv-2013-Hierarchical Part Matching for Fine-Grained Visual Categorization
199 iccv-2013-High Quality Shape from a Single RGB-D Image under Uncalibrated Natural Illumination
200 iccv-2013-Higher Order Matching for Consistent Multiple Target Tracking
201 iccv-2013-Holistic Scene Understanding for 3D Object Detection with RGBD Cameras
202 iccv-2013-How Do You Tell a Blackbird from a Crow?
203 iccv-2013-How Related Exemplars Help Complex Event Detection in Web Videos?
204 iccv-2013-Human Attribute Recognition by Rich Appearance Dictionary
205 iccv-2013-Human Re-identification by Matching Compositional Template with Cluster Sampling
206 iccv-2013-Hybrid Deep Learning for Face Verification
207 iccv-2013-Illuminant Chromaticity from Image Sequences
208 iccv-2013-Image Co-segmentation via Consistent Functional Maps
209 iccv-2013-Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation
210 iccv-2013-Image Retrieval Using Textual Cues
213 iccv-2013-Implied Feedback: Learning Nuances of User Behavior in Image Search
214 iccv-2013-Improving Graph Matching via Density Maximization
215 iccv-2013-Incorporating Cloud Distribution in Sky Representation
216 iccv-2013-Inferring "Dark Matter" and "Dark Energy" from Videos
217 iccv-2013-Initialization-Insensitive Visual Tracking through Voting with Salient Local Features
218 iccv-2013-Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data
219 iccv-2013-Internet Based Morphable Model
220 iccv-2013-Joint Deep Learning for Pedestrian Detection
221 iccv-2013-Joint Inverted Indexing
222 iccv-2013-Joint Learning of Discriminative Prototypes and Large Margin Nearest Neighbor Classifiers
223 iccv-2013-Joint Noise Level Estimation from Personal Photo Collections
224 iccv-2013-Joint Optimization for Consistent Multiple Graph Matching
225 iccv-2013-Joint Segmentation and Pose Tracking of Human in Natural Videos
226 iccv-2013-Joint Subspace Stabilization for Stereoscopic Video
227 iccv-2013-Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning
228 iccv-2013-Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences
229 iccv-2013-Large-Scale Video Hashing via Structure Learning
230 iccv-2013-Latent Data Association: Bayesian Model Selection for Multi-target Tracking
231 iccv-2013-Latent Multitask Learning for View-Invariant Action Recognition
232 iccv-2013-Latent Space Sparse Subspace Clustering
233 iccv-2013-Latent Task Adaptation with Large-Scale Hierarchies
234 iccv-2013-Learning CRFs for Image Parsing with Adaptive Subgradient Descent
235 iccv-2013-Learning Coupled Feature Spaces for Cross-Modal Matching
236 iccv-2013-Learning Discriminative Part Detectors for Image Classification and Cosegmentation
237 iccv-2013-Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes
238 iccv-2013-Learning Graphs to Match
239 iccv-2013-Learning Hash Codes with Listwise Supervision
240 iccv-2013-Learning Maximum Margin Temporal Warping for Action Recognition
241 iccv-2013-Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection
242 iccv-2013-Learning People Detectors for Tracking in Crowded Scenes
243 iccv-2013-Learning Slow Features for Behaviour Analysis
244 iccv-2013-Learning View-Invariant Sparse Representations for Cross-View Action Recognition
245 iccv-2013-Learning a Dictionary of Shape Epitomes with Applications to Image Labeling
246 iccv-2013-Learning the Visual Interpretation of Sentences
247 iccv-2013-Learning to Predict Gaze in Egocentric Video
248 iccv-2013-Learning to Rank Using Privileged Information
249 iccv-2013-Learning to Share Latent Tasks for Action Recognition
250 iccv-2013-Lifting 3D Manhattan Lines from a Single Image
251 iccv-2013-Like Father, Like Son: Facial Expression Dynamics for Kinship Verification
252 iccv-2013-Line Assisted Light Field Triangulation and Stereo Matching
254 iccv-2013-Live Metric 3D Reconstruction on Mobile Phones
255 iccv-2013-Local Signal Equalization for Correspondence Matching
256 iccv-2013-Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation
257 iccv-2013-Log-Euclidean Kernels for Sparse Representation and Dictionary Learning
258 iccv-2013-Low-Rank Sparse Coding for Image Classification
259 iccv-2013-Manifold Based Face Synthesis from Sparse Samples
260 iccv-2013-Manipulation Pattern Discovery: A Nonparametric Bayesian Approach
261 iccv-2013-Markov Network-Based Unified Classifier for Face Identification
262 iccv-2013-Matching Dry to Wet Materials
263 iccv-2013-Measuring Flow Complexity in Videos
264 iccv-2013-Minimal Basis Facility Location for Subspace Segmentation
265 iccv-2013-Mining Motion Atoms and Phrases for Complex Action Recognition
267 iccv-2013-Model Recommendation with Virtual Probes for Egocentric Hand Detection
268 iccv-2013-Modeling 4D Human-Object Interactions for Event and Object Recognition
269 iccv-2013-Modeling Occlusion by Discriminative AND-OR Structures
270 iccv-2013-Modeling Self-Occlusions in Dynamic Shape and Appearance Tracking
272 iccv-2013-Modifying the Memorability of Face Photographs
273 iccv-2013-Monocular Image 3D Human Pose Estimation under Self-Occlusion
274 iccv-2013-Monte Carlo Tree Search for Scheduling Activity Recognition
275 iccv-2013-Motion-Aware KNN Laplacian for Video Matting
276 iccv-2013-Multi-attributed Dictionary Learning for Sparse Coding
277 iccv-2013-Multi-channel Correlation Filters
278 iccv-2013-Multi-scale Topological Features for Hand Posture Representation and Analysis
279 iccv-2013-Multi-stage Contextual Deep Learning for Pedestrian Detection
280 iccv-2013-Multi-view 3D Reconstruction from Uncalibrated Radially-Symmetric Cameras
281 iccv-2013-Multi-view Normal Field Integration for 3D Reconstruction of Mirroring Objects
282 iccv-2013-Multi-view Object Segmentation in Space and Time
283 iccv-2013-Multiple Non-rigid Surface Detection and Registration
284 iccv-2013-Multiview Photometric Stereo Using Planar Mesh Parameterization
285 iccv-2013-NEIL: Extracting Visual Knowledge from Web Data
286 iccv-2013-NYC3DCars: A Dataset of 3D Vehicles in Geographic Context
287 iccv-2013-Neighbor-to-Neighbor Search for Fast Coding of Feature Vectors
288 iccv-2013-Nested Shape Descriptors
289 iccv-2013-Network Principles for SfM: Disambiguating Repeated Structures with Local Context
290 iccv-2013-New Graph Structured Sparsity Model for Multi-label Image Annotations
292 iccv-2013-Non-convex P-Norm Projection for Robust Sparsity
293 iccv-2013-Nonparametric Blind Super-resolution
294 iccv-2013-Offline Mobile Instance Retrieval with a Small Memory Footprint
295 iccv-2013-On One-Shot Similarity Kernels: Explicit Feature Maps and Properties
296 iccv-2013-On the Mean Curvature Flow on Graphs with Applications in Image and Manifold Processing
297 iccv-2013-Online Motion Segmentation Using Dynamic Label Propagation
298 iccv-2013-Online Robust Non-negative Dictionary Learning for Visual Tracking
299 iccv-2013-Online Video SEEDS for Temporal Window Objectness
300 iccv-2013-Optical Flow via Locally Adaptive Fusion of Complementary Data Costs
301 iccv-2013-Optimal Orthogonal Basis and Image Assimilation: Motion Modeling
302 iccv-2013-Optimization Problems for Fast AAM Fitting in-the-Wild
303 iccv-2013-Orderless Tracking through Model-Averaged Posterior Estimation
304 iccv-2013-PM-Huber: PatchMatch with Huber Regularization for Stereo Matching
305 iccv-2013-POP: Person Re-identification Post-rank Optimisation
306 iccv-2013-Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items
307 iccv-2013-Parallel Transport of Deformations in Shape Space of Elastic Surfaces
308 iccv-2013-Parsing IKEA Objects: Fine Pose Estimation
309 iccv-2013-Partial Enumeration and Curvature Regularization
310 iccv-2013-Partial Sum Minimization of Singular Values in RPCA for Low-Level Vision
311 iccv-2013-Pedestrian Parsing via Deep Decompositional Network
312 iccv-2013-Perceptual Fidelity Aware Mean Squared Error
313 iccv-2013-Person Re-identification by Salience Matching
314 iccv-2013-Perspective Motion Segmentation via Collaborative Clustering
315 iccv-2013-PhotoOCR: Reading Text in Uncontrolled Conditions
316 iccv-2013-Pictorial Human Spaces: How Well Do Humans Perceive a 3D Articulated Pose?
317 iccv-2013-Piecewise Rigid Scene Flow
318 iccv-2013-PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects
319 iccv-2013-Point-Based 3D Reconstruction of Thin Objects
320 iccv-2013-Pose-Configurable Generic Tracking of Elongated Objects
322 iccv-2013-Pose Estimation and Segmentation of People in 3D Movies
323 iccv-2013-Pose Estimation with Unknown Focal Length Using Points, Directions and Lines
324 iccv-2013-Potts Model, Parametric Maxflow and K-Submodular Functions
325 iccv-2013-Predicting Primary Gaze Behavior Using Social Saliency Fields
326 iccv-2013-Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation
327 iccv-2013-Predicting an Object Location Using a Global Image Representation
328 iccv-2013-Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation
329 iccv-2013-Progressive Multigrid Eigensolvers for Multiscale Spectral Segmentation
330 iccv-2013-Proportion Priors for Image Sequence Segmentation
331 iccv-2013-Pyramid Coding for Functional Scene Element Recognition in Video Scenes
332 iccv-2013-Quadruplet-Wise Image Similarity Learning
334 iccv-2013-Query-Adaptive Asymmetrical Dissimilarities for Visual Object Retrieval
335 iccv-2013-Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition
336 iccv-2013-Random Forests of Local Experts for Pedestrian Detection
337 iccv-2013-Random Grids: Fast Approximate Nearest Neighbors and Range Searching for Image Search
338 iccv-2013-Randomized Ensemble Tracking
339 iccv-2013-Rank Minimization across Appearance and Shape for AAM Ensemble Fitting
341 iccv-2013-Real-Time Body Tracking with One Depth Camera and Inertial Sensors
343 iccv-2013-Real-World Normal Map Capture for Nearly Flat Reflective Surfaces
344 iccv-2013-Recognising Human-Object Interaction via Exemplar Based Modelling
345 iccv-2013-Recognizing Text with Perspective Distortion in Natural Scenes
346 iccv-2013-Rectangling Stereographic Projection for Wide-Angle Image Visualization
347 iccv-2013-Recursive Estimation of the Stein Center of SPD Matrices and Its Applications
348 iccv-2013-Refractive Structure-from-Motion on Underwater Images
349 iccv-2013-Regionlets for Generic Object Detection
350 iccv-2013-Relative Attributes for Large-Scale Abandoned Object Detection
351 iccv-2013-Restoring an Image Taken through a Window Covered with Dirt or Rain
352 iccv-2013-Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees
353 iccv-2013-Revisiting the PnP Problem: A Fast, General and Optimal Solution
354 iccv-2013-Robust Dictionary Learning by Error Source Decomposition
355 iccv-2013-Robust Face Landmark Estimation under Occlusion
356 iccv-2013-Robust Feature Set Matching for Partial Face Recognition
357 iccv-2013-Robust Matrix Factorization with Unknown Noise
358 iccv-2013-Robust Non-parametric Data Fitting for Correspondence Modeling
359 iccv-2013-Robust Object Tracking with Online Multi-lifespan Dictionary Learning
360 iccv-2013-Robust Subspace Clustering via Half-Quadratic Minimization
361 iccv-2013-Robust Trajectory Clustering for Motion Segmentation
362 iccv-2013-Robust Tucker Tensor Decomposition for Effective Image Representation
363 iccv-2013-Rolling Shutter Stereo
364 iccv-2013-SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition
365 iccv-2013-SIFTpack: A Compact Representation for Efficient SIFT Matching
366 iccv-2013-STAR3D: Simultaneous Tracking and Reconstruction of 3D Objects Using RGB-D Data
367 iccv-2013-SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
368 iccv-2013-SYM-FISH: A Symmetry-Aware Flip Invariant Sketch Histogram Shape Descriptor
369 iccv-2013-Saliency Detection: A Boolean Map Approach
370 iccv-2013-Saliency Detection in Large Point Sets
371 iccv-2013-Saliency Detection via Absorbing Markov Chain
372 iccv-2013-Saliency Detection via Dense and Sparse Reconstruction
373 iccv-2013-Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics
374 iccv-2013-Salient Region Detection by UFO: Uniqueness, Focusness and Objectness
375 iccv-2013-Scene Collaging: Analysis and Synthesis of Natural Images with Semantic Layers
376 iccv-2013-Scene Text Localization and Recognition with Oriented Stroke Detection
377 iccv-2013-Segmentation Driven Object Detection with Fisher Vectors
378 iccv-2013-Semantic-Aware Co-indexing for Image Retrieval
379 iccv-2013-Semantic Segmentation without Annotating Segments
380 iccv-2013-Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes
381 iccv-2013-Semantically-Based Human Scanpath Estimation with HMMs
382 iccv-2013-Semi-dense Visual Odometry for a Monocular Camera
383 iccv-2013-Semi-supervised Learning for Large Scale Image Cosegmentation
384 iccv-2013-Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization
387 iccv-2013-Shape Anchors for Data-Driven Multi-view Reconstruction
388 iccv-2013-Shape Index Descriptors Applied to Texture-Based Galaxy Analysis
389 iccv-2013-Shortest Paths with Curvature and Torsion
390 iccv-2013-Shufflets: Shared Mid-level Parts for Fast Object Detection
391 iccv-2013-Sieving Regression Forest Votes for Facial Feature Detection in the Wild
392 iccv-2013-Similarity Metric Learning for Face Recognition
393 iccv-2013-Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos
394 iccv-2013-Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal
395 iccv-2013-Slice Sampling Particle Belief Propagation
396 iccv-2013-Space-Time Robust Representation for Action Recognition
397 iccv-2013-Space-Time Tradeoffs in Photo Sequencing
399 iccv-2013-Spoken Attributes: Mixing Binary and Relative Attributes to Say the Right Thing
400 iccv-2013-Stable Hyper-pooling and Query Expansion for Event Detection
402 iccv-2013-Street View Motion-from-Structure-from-Motion
403 iccv-2013-Strong Appearance and Expressive Spatial Models for Human Pose Estimation
404 iccv-2013-Structured Forests for Fast Edge Detection
405 iccv-2013-Structured Light in Sunlight
406 iccv-2013-Style-Aware Mid-level Representation for Discovering Visual Connections in Space and Time
407 iccv-2013-Subpixel Scanning Invariant to Indirect Lighting Using Quadratic Code Length
408 iccv-2013-Super-resolution via Transform-Invariant Group-Sparse Regularization
409 iccv-2013-Supervised Binary Hash Code Learning with Jensen Shannon Divergence
410 iccv-2013-Support Surface Prediction in Indoor Scenes
411 iccv-2013-Symbiotic Segmentation and Part Localization for Fine-Grained Categorization
413 iccv-2013-Target-Driven Moire Pattern Synthesis by Phase Modulation
414 iccv-2013-Temporally Consistent Superpixels
416 iccv-2013-The Interestingness of Images
418 iccv-2013-The Way They Move: Tracking Multiple Targets with Similar Appearance
419 iccv-2013-To Aggregate or Not to aggregate: Selective Match Kernels for Image Search
420 iccv-2013-Topology-Constrained Layered Tracking with Latent Flow
421 iccv-2013-Total Variation Regularization for Functions with Values in a Manifold
422 iccv-2013-Toward Guaranteed Illumination Models for Non-convex Objects
423 iccv-2013-Towards Motion Aware Light Field Video for Dynamic Scenes
424 iccv-2013-Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines
425 iccv-2013-Tracking via Robust Multi-task Multi-view Joint Sparse Representation
426 iccv-2013-Training Deformable Part Models with Decorrelated Features
427 iccv-2013-Transfer Feature Learning with Joint Distribution Adaptation
428 iccv-2013-Translating Video Content to Natural Language Descriptions
429 iccv-2013-Tree Shape Priors with Connectivity Constraints Using Convex Relaxation on General Graphs
430 iccv-2013-Two-Point Gait: Decoupling Gait from Body Shape
433 iccv-2013-Understanding High-Level Semantics by Modeling Traffic Patterns
435 iccv-2013-Unsupervised Domain Adaptation by Domain Invariant Projection
436 iccv-2013-Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach
437 iccv-2013-Unsupervised Random Forest Manifold Alignment for Lipreading
438 iccv-2013-Unsupervised Visual Domain Adaptation Using Subspace Alignment
439 iccv-2013-Video Co-segmentation for Meaningful Action Extraction
440 iccv-2013-Video Event Understanding Using Natural Language Descriptions
441 iccv-2013-Video Motion for Every Visible Point
442 iccv-2013-Video Segmentation by Tracking Many Figure-Ground Segments
443 iccv-2013-Video Synopsis by Heterogeneous Multi-source Correlation
444 iccv-2013-Viewing Real-World Faces in 3D
445 iccv-2013-Visual Reranking through Weakly Supervised Multi-graph Learning
446 iccv-2013-Visual Semantic Complex Network for Web Images
447 iccv-2013-Volumetric Semantic Segmentation Using Pyramid Context Features
449 iccv-2013-What Do You Do? Occupation Recognition in a Photo via Social Context
451 iccv-2013-Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions