Yu Xiang, Wongun Choi, Yuanqing Lin, Silvio Savarese

Abstract: Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark and the outdoor-scene dataset. We improve state-of-the-art results on car detection and pose estimation with notable margins (6% in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.

@InProceedings{Xiang_2015_CVPR,author = {Xiang, Yu and Choi, Wongun and Lin, Yuanqing and Savarese, Silvio},title = {Data-Driven 3D Voxel Patterns for Object Category Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang

Abstract: This paper addresses two issues hindering the advances in accurate image alignment. First, the performance of descriptor-based approaches to image alignment relies on the chosen descriptor, but the optimal descriptor typically varies from image to image, or even pixel to pixel. Second, the neighborhood structure for smoothness enforcement is usually predefined before alignment. However, object boundaries are often better discovered during alignment. The proposed approach tackles the two issues by adaptive descriptor selection and dynamic neighborhood construction. Specifically, we associate each pixel to be aligned with an affine transformation, and integrate the learning of the pixel-specific transformations into image alignment. The transformations serve as the common domain for descriptor fusion, since the local consensus of each descriptor can be estimated by accessing the corresponding affine transformation. It allows us to pick the most plausible descriptor for aligning each pixel. On the other hand, more object-aware neighborhoods can be produced by referencing the consistency between the learned affine transformations of neighboring pixels. The promising results on popular image alignment benchmarks manifests the effectiveness of our approach.

@InProceedings{Hsu_2015_CVPR,author = {Hsu, Kuang-Jui and Lin, Yen-Yu and Chuang, Yung-Yu},title = {Robust Image Alignment With Multiple Feature Descriptors and Matching-Guided Neighborhoods},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao

Abstract: 3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.

@InProceedings{Wu_2015_CVPR,author = {Wu, Zhirong and Song, Shuran and Khosla, Aditya and Yu, Fisher and Zhang, Linguang and Tang, Xiaoou and Xiao, Jianxiong},title = {3D ShapeNets: A Deep Representation for Volumetric Shapes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Brendan F. Klare, Ben Klein, Emma Taborsky, Austin Blanton, Jordan Cheney, Kristen Allen, Patrick Grother, Alan Mah, Anil K. Jain

Abstract: Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search)  and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.

@InProceedings{Klare_2015_CVPR,author = {Klare, Brendan F. and Klein, Ben and Taborsky, Emma and Blanton, Austin and Cheney, Jordan and Allen, Kristen and Grother, Patrick and Mah, Alan and Jain, Anil K.},title = {Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Michael W. Tao, Pratul P. Srinivasan, Jitendra Malik, Szymon Rusinkiewicz, Ravi Ramamoorthi

Abstract: Light-field cameras are now used in consumer and industrial applications. Recent papers and products have demonstrated practical depth recovery algorithms from a passive single-shot capture. However, current light field capture devices have narrow baselines and constrained spatial resolution; therefore, the accuracy of depth recovery is limited, requiring heavy regularization and producing planar depths that do not resemble the actual geometry. Using shading information is essential to improve the shape estimation. We develop an improved technique for local shape estimation from defocus and correspondence cues, and show how shading can be used to further refine the depth.  Light-field cameras are able to capture both spatial and angular data, suitable for refocusing. By locally refocusing each spatial pixel to its respective estimated depth, we produce an all-in-focus image where all viewpoints converge onto a point in the scene. Therefore, the angular pixels have angular coherence, which exhibits three properties: photo consistency, depth consistency, and shading consistency. We propose a new framework that uses angular coherence to optimize depth and shading. The optimization framework estimates both general lighting in natural scenes and shading to improve depth regularization. Our method outperforms current state-of-the-art light-field depth estimation algorithms in multiple scenarios, including real images.

@InProceedings{Tao_2015_CVPR,author = {Tao, Michael W. and Srinivasan, Pratul P. and Malik, Jitendra and Rusinkiewicz, Szymon and Ramamoorthi, Ravi},title = {Depth From Shading, Defocus, and Correspondence Using Light-Field Angular Coherence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Graph-based computer vision applications rely critically on similarity metrics which compute the pairwise similarity between any pair of vertices on graphs. This paper investigates the fundamental design of commonly used similarity metrics, and provides new insights to guide their use in practice. In particular, we introduce a family of similarity metrics in the form of (L+\alpha\Lambda)^{-1}, where L is the graph Laplacian, \Lambda is a positive diagonal matrix acting as a regularizer, and \alpha is a positive balancing factor. Such metrics respect graph topology when \alpha is small, and reproduce well-known metrics such as hitting times and the pseudo-inverse of graph Laplacian with different regularizer \Lambda.     This paper is the first to analyze the important impact of selecting \Lambda in retrieving the local cluster from a seed. We find that different \Lambda can lead to surprisingly complementary behaviors: \Lambda = D (degree matrix) can reliably extract the cluster of a query if it is sparser than surrounding clusters, while \Lambda = I (identity matrix) is preferred if it is denser than surrounding clusters. Since in practice there is no reliable way to determine the local density in order to select the right model, we propose a new design of \Lambda that automatically adapts to the local density. Experiments on image retrieval verify our theoretical arguments and confirm the benefit of the proposed metric. We expect the insights of our theory to provide guidelines for more applications in computer vision and other domains.

@InProceedings{Wu_2015_CVPR,author = {Wu, Xiao-Ming and Li, Zhenguo and Chang, Shih-Fu},title = {New Insights Into Laplacian Similarity Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Automatic image annotation is a highly valuable tool for image search, retrieval and archival systems. In the absence of an annotation tool, such systems have to rely on either users' input or large amount of text on the webpage of the image, to acquire its textual description. Users may provide insufficient/noisy tags and all the text on the webpage may not be a description or an explanation of the accompanying image. Therefore, it is of extreme importance to develop efficient tools for automatic annotation of images with correct and sufficient tags. The context of the image plays a significant role in this process, along with the content of the image. A suitable quantification of the context of the image may reduce the semantic gap between visual features and appropriate textual description of the image. In this paper, we present an unsupervised feature-independent quantification of the context of the image through tensor decomposition. We incorporate the estimated context as prior knowledge in the process of automatic image annotation. Evaluation of the predicted annotations provides evidence of the effectiveness of our feature-independent context estimation method.

@InProceedings{Tariq_2015_CVPR,author = {Tariq, Amara and Foroosh, Hassan},title = {Feature-Independent Context Estimation for Automatic Image Annotation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abhishek Kar, Shubham Tulsiani, Joao Carreira, Jitendra Malik

Abstract: Object reconstruction from a single image -- in the wild -- is a problem where we can make progress and get meaningful results today. This is the main message of this paper, which introduces an automated pipeline with pixels as inputs and 3D surfaces of various rigid categories as outputs in images of realistic scenes. At the core of our approach are deformable 3D models that can be learned from 2D annotations available in existing object detection datasets, that can be driven by noisy automatic object segmentations and which we complement with a bottom-up module for recovering high-frequency shape details. We perform a comprehensive quantitative analysis and ablation study of our approach using the recently introduced PASCAL 3D+ dataset and show very encouraging automatic reconstructions on PASCAL VOC.

@InProceedings{Kar_2015_CVPR,author = {Kar, Abhishek and Tulsiani, Shubham and Carreira, Joao and Malik, Jitendra},title = {Category-Specific Object Reconstruction From a Single Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh

Abstract: When data have a complex manifold structure or the characteristics of data evolve over time, it is unrealistic to expect a graph-based semi-supervised learning method to achieve flawless classification given a small number of initial annotations. To address this issue with minimal human interventions, we propose (i) a sample selection criterion used for \textit{active} query of informative samples by minimizing the expected prediction error, and (ii) an efficient {\it correction propagation} method that propagates human correction on selected samples over a {\it gradually-augmented graph} to unlabeled samples without rebuilding the affinity graph. Experimental results conducted on three real world datasets validate that our active sample selection and correction propagation algorithm quickly reaches high quality classification results with minimal human interventions.

@InProceedings{Su_2015_CVPR,author = {Su, Hang and Yin, Zhaozheng and Kanade, Takeo and Huh, Seungil},title = {Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiangyu Zhang, Jianhua Zou, Xiang Ming, Kaiming He, Jian Sun

Abstract: This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs). Unlike existing methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We minimize the reconstruction error of the nonlinear responses, subject to a low-rank constraint which helps to reduce the complexity of filters. We develop an effective solution to this constrained nonlinear optimization problem. An algorithm is also presented for reducing the accumulated error when multiple layers are approximated. A whole-model speedup ratio of 4x is demonstrated   on a large network trained for ImageNet, while the top-5 error rate is only increased by 0.9%. Our accelerated model has a comparably fast speed as the "AlexNet", but is 4.7% more accurate.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Xiangyu and Zou, Jianhua and Ming, Xiang and He, Kaiming and Sun, Jian},title = {Efficient and Accurate Approximations of Nonlinear Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Casual Stereoscopic Panorama Stitching

Abstract: This paper presents a method for stitching stereoscopic panoramas from stereo images casually taken using a stereo camera. This method addresses three challenges of stereoscopic image stitching: how to handle parallax, how to stitch the left- and right-view panorama consistently, and how to take care of disparity during stitching. This method addresses these challenges using a three-step approach. First, we employ a state-of-the-art stitching algorithm that handles parallax well to stitch the left views of input stereo images and create the left view of the final stereoscopic panorama. Second, we stitch the input disparity maps to obtain the target disparity map for the stereoscopic panorama by solving a Poisson's equation. This target disparity map is optimized such that there are no vertical disparities and the original perceived depth distribution is preserved. Finally, we warp the right views of the input stereo images and stitch them into the right view of the final stereoscopic panorama according to the target disparity map. The stitching of the right views is formulated as a labeling problem that is constrained by the stitching of the left views to make the left- and right-view panorama consistent to avoid retinal rivalry. Our experiments show that our method can effectively stitch casually taken stereo images and produce high-quality stereoscopic panoramas that deliver a pleasant stereoscopic 3D viewing experience.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Fan and Liu, Feng},title = {Casual Stereoscopic Panorama Stitching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Gunhee Kim, Seungwhan Moon, Leonid Sigal

Abstract: We propose a method to rank and retrieve image sequences from a natural language text query, consisting of multiple sentences or paragraphs. One of the method's key applications is to visualize visitors' text-only reviews on TRIPADVISOR or YELP, by automatically retrieving the most illustrative image sequences. While most previous work has dealt with the relations between a natural language sentence and an image or a video, our work extends to the relations between paragraphs and image sequences. Our approach leverages the vast user-generated resource of blog posts and photo streams on the Web. We use blog posts as text-image parallel training data that co-locate informative text with representative images that are carefully selected by users. We exploit large-scale photo streams to augment the image samples for retrieval. We design a latent structural SVM framework to learn the semantic relevance relations between text and image sequences. We present both quantitative and qualitative results on the newly created DISNEYLAND dataset.

@InProceedings{Kim_2015_CVPR,author = {Kim, Gunhee and Moon, Seungwhan and Sigal, Leonid},title = {Ranking and Retrieval of Image Sequences From Multiple Paragraph Queries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Andras Bodis-Szomoru, Hayko Riemenschneider, Luc Van Gool

Abstract: Multi-View-Stereo (MVS) methods aim for the highest detail possible, however, such detail is often not required. In this work, we propose a novel surface reconstruction method based on image edges, superpixels and second-order smoothness constraints, producing meshes comparable to classic MVS surfaces in quality but orders of magnitudes faster. Our method performs per-view dense depth optimization directly over sparse 3D Ground Control Points (GCPs), hence, removing the need for view pairing, image rectification, and stereo depth estimation, and allowing for full per-image parallelization. We use Structure-from-Motion (SfM) points as GCPs, but the method is not specific to these, e.g.~LiDAR or RGB-D can also be used. The resulting meshes are compact and inherently edge-aligned with image gradients, enabling good-quality lightweight per-face flat renderings. Our experiments demonstrate on a variety of 3D datasets the superiority in speed and competitive surface quality.

@InProceedings{Bodis-Szomoru_2015_CVPR,author = {Bodis-Szomoru, Andras and Riemenschneider, Hayko and Van Gool, Luc},title = {Superpixel Meshes for Fast Edge-Preserving Surface Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman

Abstract: We propose a novel method for template matching in unconstrained environments. Its essence is the Best Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on a count of Best Buddies Pairs (BBPs)--pairs of points in which each one is the nearest neighbor of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset.

@InProceedings{Dekel_2015_CVPR,author = {Dekel, Tali and Oron, Shaul and Rubinstein, Michael and Avidan, Shai and Freeman, William T.},title = {Best-Buddies Similarity for Robust Template Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tatsunori Taniai, Yasuyuki Matsushita, Takeshi Naemura

Abstract: We propose an efficient and general purpose energy optimization method for binary variable energies used in various low-level vision tasks. The proposed method can be used for broad classes of higher-order and pairwise non-submodular functions. We first revisit a submodular-supermodular procedure (SSP) [Narasimhan05], which is previously studied for higher-order energy optimization. We then present our method as generalization of SSP, which is further shown to generalize several state-of-the-art techniques for higher-order and pairwise non-submodular functions [Ayed13, Gorelick14, Tang14]. In the experiments, we apply our method to image segmentation, deconvolution, and binarization, and show improvements over state-of-the-art methods.

@InProceedings{Taniai_2015_CVPR,author = {Taniai, Tatsunori and Matsushita, Yasuyuki and Naemura, Takeshi},title = {Superdifferential Cuts for Binary Energies},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Davide Conigliaro, Paolo Rota, Francesco Setti, Chiara Bassetti, Nicola Conci, Nicu Sebe, Marco Cristani

Abstract: The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people "interested in watching something specific that they came to see". This can be found at the stadiums, amphitheaters, cinema, etc. In particular, we propose a novel dataset, the Spectators Hockey (S-Hock), which deals with 4 hockey matches during an international tournament. In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc. The labeling focused on the game field also, permitting to relate what is going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard applications as people counting and head pose estimation but also for novel tasks as spectator categorization. For all of these we provide protocols and baseline results, encouraging further research.

@InProceedings{Conigliaro_2015_CVPR,author = {Conigliaro, Davide and Rota, Paolo and Setti, Francesco and Bassetti, Chiara and Conci, Nicola and Sebe, Nicu and Cristani, Marco},title = {The S-Hock Dataset: Analyzing Crowds at the Stadium},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wen Wang, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen

Abstract: This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our goal is to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as Gaussian Mixture Model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. Through these kernels, a weighted Kernel Discriminant Analysis is finally devised which treats the Gaussians in GMMs as samples and their prior probabilities as sample weights. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.

@InProceedings{Wang_2015_CVPR,author = {Wang, Wen and Wang, Ruiping and Huang, Zhiwu and Shan, Shiguang and Chen, Xilin},title = {Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Georgios Georgiadis, Alessandro Chiuso, Stefano Soatto

Abstract: In texture synthesis and classification, algorithms require a small texture to be provided as an input, which is assumed to be representative of a larger region to be re-synthesized or categorized. We focus on how to characterize such textures and automatically retrieve them. Most works generate these small input textures manually by cropping, which does not ensure maximal compression, nor that the selection is the best representative of the original. We construct a new representation that compactly summarizes a texture, while using less storage, that can be used for texture compression and synthesis. We also demonstrate how the representation can be integrated in our proposed video texture synthesis algorithm to generate novel instances of textures and video hole-filling. Finally, we propose a novel criterion that measures structural and statistical dissimilarity between textures.

@InProceedings{Georgiadis_2015_CVPR,author = {Georgiadis, Georgios and Chiuso, Alessandro and Soatto, Stefano},title = {Texture Representations for Image and Video Synthesis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We present a novel learning-based framework for shadow detection from a single image. The local structure of shadow boundaries as well as the global interactions of the shadow and non-shadow regions remain largely unexploited by previous learning-based approaches. In this paper, we propose an efficient structured labelling framework for shadow detection from a single image. A convolutional Neural Networks framework is designed to capture the local structure information of shadow edge and to learn the most relevant features. We further propose and formulate a global shadow optimization framework which can model the complex global interactions over the shadow and light regions. Using the shadow edges detected by our proposed method, the shadow map can be solved by efficient least-square optimization. Our proposed framework is efficient and achieves state-of-the-art results on the major shadow benchmark databases collected under a variety of conditions.

@InProceedings{Shen_2015_CVPR,author = {Shen, Li and Wee Chua, Teck and Leman, Karianto},title = {Shadow Optimization From Structured Deep Edge Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Damien Teney, Matthew Brown, Dmitry Kit, Peter Hall

Abstract: This paper addresses the segmentation of videos with arbitrary motion, including dynamic textures, using novel motion features and a supervised learning approach. Dynamic textures are commonplace in natural scenes, and exhibit complex patterns of appearance and motion (e.g. water, smoke, swaying foliage). These are difficult for existing segmentation algorithms, often violate the brightness constancy assumption needed for optical flow, and have complex segment characteristics beyond uniform appearance or motion. Our solution uses custom spatiotemporal filters that capture texture and motion cues, along with a novel metric-learning framework that optimizes this representation for specific objects and scenes. This is used within a hierarchical, graph-based segmentation setting, yielding state-of-the-art results for dynamic texture segmentation. We also demonstrate the applicability of our approach to general object and motion segmentation, showing significant improvements over unsupervised segmentation and results comparable to the best task specific approaches.

@InProceedings{Teney_2015_CVPR,author = {Teney, Damien and Brown, Matthew and Kit, Dmitry and Hall, Peter},title = {Learning Similarity Metrics for Dynamic Scene Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Maximilian Baust, Laurent Demaret, Martin Storath, Nassir Navab, Andreas Weinmann

Abstract: This paper introduces the concept of shape signals, i.e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals. The proposed formulation can be seen as the shape-valued generalization of the Rudin-Osher-Fatemi (ROF) functional for intensity images. We derive a variant of the classical finite-dimensional representation of Kendall, but our framework is generic in the sense that it can be combined with any shape space. This representation allows for the explicit computation of geodesics and thus facilitates the efficient numerical treatment of the variational formulation by means of the cyclic proximal point algorithm. Similar to the ROF-functional, we demonstrate experimentally that l_1-type penalties both for data fidelity term and regularizer perform best in regularizing shape signals. Finally, we show applications of our method to shape signals obtained from synthetic, photometric, and medical data sets.

@InProceedings{Baust_2015_CVPR,author = {Baust, Maximilian and Demaret, Laurent and Storath, Martin and Navab, Nassir and Weinmann, Andreas},title = {Total Variation Regularization of Shape Signals},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Baohua Li, Ying Zhang, Zhouchen Lin, Huchuan Lu

Abstract: Subspace clustering is a problem of finding a multisubspace representation that best fits sample points drawn from a high-dimensional space. The existing clustering models generally adopt different norms to describe noise, which is equivalent to assuming that the data are corrupted by specific types of noise. In practice, however, noise is much more complex. So it is inappropriate to simply use a certain norm to model noise. Therefore, we propose Mixture of Gaussian Regression (MoG Regression) for subspace clustering by modeling noise as a Mixture of Gaussians (MoG). The MoG Regression provides an effective way to model a much broader range of noise distributions. As a result, the obtained affinity matrix is better at characterizing the structure of data in real applications. Experimental results on multiple datasets demonstrate that MoG Regression significantly outperforms state-of-the-art subspace clustering methods.

@InProceedings{Li_2015_CVPR,author = {Li, Baohua and Zhang, Ying and Lin, Zhouchen and Lu, Huchuan},title = {Subspace Clustering by Mixture of Gaussian Regression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Seungryong Kim, Dongbo Min, Bumsub Ham, Seungchul Ryu, Minh N. Do, Kwanghoon Sohn

Abstract: Establishing dense visual correspondence between multiple images is a fundamental task in many applications of computer vision and computational photography. Classical approaches, which aim to estimate dense stereo and optical flow fields for images adjacent in viewpoint or in time, have been dramatically advanced in recent studies. However, finding reliable visual correspondence in multi-modal or multi-spectral images still remains unsolved. In this paper, we propose a new dense matching descriptor, called dense adaptive self-correlation (DASC), to effectively address this kind of matching scenarios. Based on the observation that a self-similarity existing within images is less sensitive to modality variations, we define a novel descriptor with a series of an adaptive self-correlation similarity for patches within a local support window. To further improve the matching quality and runtime efficiency, we propose a patch-wise receptive field pooling, in which a sampling pattern is optimized with a discriminative learning. Moreover, the computational redundancy that arises when computing densely sampled descriptor over an entire image is dramatically reduced by applying fast edge-aware filtering. Experiments demonstrate the outstanding performance of the DASC descriptor in many cases of multi-modal and multi-spectral correspondence.

@InProceedings{Kim_2015_CVPR,author = {Kim, Seungryong and Min, Dongbo and Ham, Bumsub and Ryu, Seungchul and Do, Minh N. and Sohn, Kwanghoon},title = {DASC: Dense Adaptive Self-Correlation Descriptor for Multi-Modal and Multi-Spectral Correspondence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Horst Possegger, Thomas Mauthner, Horst Bischof

Abstract: In this paper, we address the problem of model-free online object tracking based on color representations. According to the findings of recent benchmark evaluations, such trackers often tend to drift towards regions which exhibit a similar appearance compared to the object of interest. To overcome this limitation, we propose an efficient discriminative object model which allows us to identify potentially distracting regions in advance. Furthermore, we exploit this knowledge to adapt the object representation beforehand so that distractors are suppressed and the risk of drifting is significantly reduced. We evaluate our approach on recent online tracking benchmark datasets demonstrating state-of-the-art results. In particular, our approach performs favorably both in terms of accuracy and robustness compared to recent tracking algorithms. Moreover, the proposed approach allows for an efficient implementation to enable online object tracking in real-time.

@InProceedings{Possegger_2015_CVPR,author = {Possegger, Horst and Mauthner, Thomas and Bischof, Horst},title = {In Defense of Color-Based Model-Free Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.

@InProceedings{Russakovsky_2015_CVPR,author = {Russakovsky, Olga and Li, Li-Jia and Fei-Fei, Li},title = {Best of Both Worlds: Human-Machine Collaboration for Object Annotation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zygmunt L. Szpak, Wojciech Chojnacki, Anton van den Hengel

Abstract: The estimation of multiple homographies between two piecewise planar views of a rigid scene is often assumed to be a solved problem. We show that contrary to popular opinion various crucial aspects of the task have not been adequately emphasised. We are motivated by a growing body of literature in robust multi-structure estimation that purports to solve the multi-homography estimation problem but in fact does not. We demonstrate that the estimation of multiple homographies is an ill-solved problem by deriving new constraints that a set of mutually compatible homographies must satisfy, and by showing that homographies estimated with prevailing methods fail to satisfy the requisite constraints on real-world data. We also explain why incompatible homographies imply inconsistent epipolar geometries. The arguments and experiments presented in this paper signal the need for a new generation of robust multi-structure estimation methods that have the capacity to enforce constraints on projective entities such as homography matrices.

@InProceedings{Szpak_2015_CVPR,author = {Szpak, Zygmunt L. and Chojnacki, Wojciech and van den Hengel, Anton},title = {Robust Multiple Homography Estimation: An Ill-Solved Problem},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei

Abstract: In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation framework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant low-dimensional structures across domains to correct data distribution mismatch and leverages available unlabeled target examples to exploit the underlying intrinsic information in the target domain. Specifically, SDASL conducts the learning by simultaneously minimizing the classification error, preserving the structure within and across domains, and restricting similarity defined on unlabeled target examples. Encouraging results are reported for two challenging domain transfer tasks (including image-to-image and image-to-video transfers) on several standard datasets in the context of both image object recognition and video concept detection.

@InProceedings{Yao_2015_CVPR,author = {Yao, Ting and Pan, Yingwei and Ngo, Chong-Wah and Li, Houqiang and Mei, Tao},title = {Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

Abstract: We propose an unsupervised approach for discovering characteristic motion patterns in videos of highly articulated objects performing natural, unscripted behaviors, such as tigers in the wild. We discover consistent patterns in a bottom-up manner by analyzing the relative displacements of large numbers of ordered trajectory pairs through time, such that each trajectory is attached to a different moving part on the object. The pairs of trajectories descriptor relies entirely on motion and is more discriminative than state-of-the-art features that employ single trajectories. Our method generates temporal video intervals, each automatically trimmed to one instance of the discovered behavior, and clusters them by type (e.g., running, turning head, drinking water). We present experiments on two datasets: dogs from YouTube-Objects and a new dataset of National Geographic tiger videos. Results confirm that our proposed descriptor outperforms existing appearance- and trajectory-based descriptors (e.g., HOG and DTFs) on both datasets and enables us to segment unconstrained animal video into intervals containing single behaviors.

@InProceedings{Pero_2015_CVPR,author = {Del Pero, Luca and Ricco, Susanna and Sukthankar, Rahul and Ferrari, Vittorio},title = {Articulated Motion Discovery Using Pairs of Trajectories},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Florian Bernard, Johan Thunberg, Peter Gemmar, Frank Hertel, Andreas Husch, Jorge Goncalves

Abstract: The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail.  Based on the observation that the underlying noise-free transformations can be retrieved from the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are transitively consistent.   Simulations demonstrate that for noisy transformations, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results.

@InProceedings{Bernard_2015_CVPR,author = {Bernard, Florian and Thunberg, Johan and Gemmar, Peter and Hertel, Frank and Husch, Andreas and Goncalves, Jorge},title = {A Solution for Multi-Alignment by Transformation Synchronisation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yongfang Cheng, Jose A. Lopez, Octavia Camps, Mario Sznaier

Abstract: This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem  can be formulated as a  constrained polynomial optimization, for which a  monotonically convergent sequence of tractable convex relaxations can be obtained by exploiting  recent developments in sparse polynomial optimization. Further, these results allow for deriving conditions certifying that a finite order relaxation has converged to a solution.  A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise,  co-ocurrences, and percentage of outliers. These results are illustrated  with several examples where the proposed algorithm is shown to outperform existing approaches.

@InProceedings{Cheng_2015_CVPR,author = {Cheng, Yongfang and Lopez, Jose A. and Camps, Octavia and Sznaier, Mario},title = {A Convex Optimization Approach to Robust Fundamental Matrix Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt

Abstract: In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points. Existing approaches attempt to exploit this relationship information implicitly by enforcing smoothness on function evaluations only. However, what happens if we explicitly regularize the relationships between function evaluations? Inspired by homophily, we regularize based on a smooth relationship function, either defined from the data or with labels. In experiments, we demonstrate that this significantly improves the performance of state-of-the-art algorithms in semi-supervised classification and in spectral data embedding for constrained clustering and dimensionality reduction.

@InProceedings{Kim_2015_CVPR,author = {In Kim, Kwang and Tompkin, James and Pfister, Hanspeter and Theobalt, Christian},title = {Semi-Supervised Learning With Explicit Relationship Regularization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper, we propose a sequential solution to simultaneously estimate camera pose and non-rigid 3D shape from a monocular video. In contrast to most existing approaches that rely on global representations of the shape, we model the object at a local level, as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency of the estimated shape and camera poses. The resulting approach is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, while it does not require any training data at all. Validation is done in a variety of real video sequences, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our system is shown to perform comparable to competing batch, computationally expensive, methods and shows remarkable improvement with respect to the sequential ones.

@InProceedings{Agudo_2015_CVPR,author = {Agudo, Antonio and Moreno-Noguer, Francesc},title = {Simultaneous Pose and Non-Rigid Shape With Particle Dynamics},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Shengcai Liao, Yang Hu, Xiangyu Zhu, Stan Z. Li

Abstract: Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.

@InProceedings{Liao_2015_CVPR,author = {Liao, Shengcai and Hu, Yang and Zhu, Xiangyu and Li, Stan Z.},title = {Person Re-Identification by Local Maximal Occurrence Representation and Metric Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Kaili Zhao, Wen-Sheng Chu, Fernando De la Torre, Jeffrey F. Cohn, Honggang Zhang

Abstract: The face is one of the most powerful channel of non-verbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS).  FACS segments the visible effects of facial muscle activation into 30+ action units (AUs).  AUs, which may occur alone and in thousands of combinations, can describe nearly all-possible facial expressions.  Most existing methods for automatic AU detection treat the problem using one-vs-all classifiers and fail to exploit dependencies among AU and facial features.  We introduce joint-patch and multi-label learning (JPML) to address these issues. JPML leverages group sparsity by selecting a sparse subset of facial patches while learning a multi-label classifier. In four of five comparisons on three diverse datasets, CK+, GFT, and BP4D, JPML produced the highest average F1 scores in comparison with state-of-the art.

@InProceedings{Zhao_2015_CVPR,author = {Zhao, Kaili and Chu, Wen-Sheng and De la Torre, Fernando and Cohn, Jeffrey F. and Zhang, Honggang},title = {Joint Patch and Multi-Label Learning for Facial Action Unit Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chao Liu, Hernando Gomez, Srinivasa Narasimhan, Artur Dubrawski, Michael R. Pinsky, Brian Zuckerbraun

Abstract: Microcirculatory monitoring plays an important role in diagnosis and treatment of critical care patients. Sidestream Dark Field (SDF) imaging devices have been used to visualize and support interpretation of the micro-vascular blood flow.  However, due to subsurface scattering within the tissue that embeds the capillaries, transparency of plasma, imaging noise and lack of features, it is difficult to obtain reliable physiological data from SDF videos. Therefore, thus far microcirculatory videos have been analyzed manually with significant input from expert clinicians. In this paper, we present a framework that automates the analysis process. It includes stages of video stabilization, enhancement, and micro-vessel extraction, in order to automatically estimate statistics of the micro blood flows from SDF videos. Our method has been validated in critical care experiments conducted carefully to record the microcirculatory blood flow in test animal subjects before, during and after induced bleeding episodes, as well as to study the effect of fluid resuscitation. Our method is able to extract microcirculatory measurements that are consistent with clinical intuition and it has a potential to become a useful tool in critical care medicine.

@InProceedings{Liu_2015_CVPR,author = {Liu, Chao and Gomez, Hernando and Narasimhan, Srinivasa and Dubrawski, Artur and Pinsky, Michael R. and Zuckerbraun, Brian},title = {Real-Time Visual Analysis of Microvascular Blood Flow for Critical Care},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Longyin Wen, Dawei Du, Zhen Lei, Stan Z. Li, Ming-Hsuan Yang

Abstract: We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. The multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. The multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

@InProceedings{Wen_2015_CVPR,author = {Wen, Longyin and Du, Dawei and Lei, Zhen and Li, Stan Z. and Yang, Ming-Hsuan},title = {JOTS: Joint Online Tracking and Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jia Xu, Lopamudra Mukherjee, Yin Li, Jamieson Warner, James M. Rehg, Vikas Singh

Abstract: With the proliferation of wearable cameras, the number of videos of users documenting their personal lives using   such devices is rapidly increasing. Since such videos may span hours,   there is an important need for mechanisms that represent the information content in a compact form (i.e., shorter videos which are more easily browsable/sharable). Motivated by these applications, this paper focuses on the problem of egocentric video summarization. Such videos are usually continuous with significant camera shake and other quality issues. Because of these reasons, there is growing consensus that direct application of standard video summarization tools to such data yields unsatisfactory performance.   In this paper, we demonstrate that using gaze tracking information (such as fixation and saccade) significantly helps the summarization task.  It allows meaningful comparison of different image frames  and enables deriving personalized   summaries (gaze provides a sense of the camera wearer's intent).   We formulate a summarization model which captures common-sense properties of a good summary, and show that   it can be solved as a submodular function maximization with partition matroid constraints, opening the door to a   rich body of work from combinatorial optimization.   We evaluate our approach on a new gaze-enabled egocentric video dataset (over 15 hours),     which will be a valuable standalone resource.

@InProceedings{Xu_2015_CVPR,author = {Xu, Jia and Mukherjee, Lopamudra and Li, Yin and Warner, Jamieson and Rehg, James M. and Singh, Vikas},title = {Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Sparse Depth Super Resolution

Abstract: We describe a method to produce detailed high resolution depth maps from aggressively subsampled depth measurements. Our method fully uses the relationship between image segmentation boundaries and depth boundaries. It uses an image combined with a low resolution depth map. 1) The image is segmented with the guidance of sparse depth samples. 2) Each segment has its depth field reconstructed independently using a novel smoothing method. 3) For videos, time-stamped samples from near frames are incorporated. The paper shows reconstruction results of super resolution from x4 to x100, while previous methods mainly work on x2 to x16. The method is tested on four different datasets and six video sequences, covering quite different regimes, and it outperforms recent state of the art methods quantitatively and qualitatively. We also demonstrate that depth maps produced by our method can be used by applications such as hand trackers, while depth maps from other methods have problems.

@InProceedings{Lu_2015_CVPR,author = {Lu, Jiajun and Forsyth, David},title = {Sparse Depth Super Resolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Illuminant estimation is a key step for computational color constancy. Instead of using the grey world or grey edge assumptions, we propose in this paper a novel method for illuminant estimation by using the information of grey pixels detected in a given color-biased image. The underlying hypothesis is that most of the natural images include some detectable pixels that are at least approximately grey, which can be reliably utilized for illuminant estimation. We first validate our assumption through comprehensive statistical evaluation on diverse collection of datasets and then put forward a novel grey pixel detection method based on the illuminant-invariant measure (IIM) in three logarithmic color channels. Then the light source color of a scene can be easily estimated from the detected grey pixels. Experimental results on four benchmark datasets (three recorded under single illuminant and one under multiple illuminants) show that the proposed method outperforms most of the state-of-the-art color constancy approaches with the inherent merit of low computational cost.

@InProceedings{Yang_2015_CVPR,author = {Yang, Kai-Fu and Gao, Shao-Bing and Li, Yong-Jie},title = {Efficient Illuminant Estimation for Color Constancy Using Grey Pixels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chenliang Xu, Shao-Hang Hsieh, Caiming Xiong, Jason J. Corso

Abstract: Can humans fly? Emphatically no. Can cars eat? Again, absolutely not. Yet, these absurd inferences result from the current disregard for particular types of actors in action understanding. There is no work we know of on simultaneously inferring actors and actions in the video, not to mention a dataset to experiment with. Our paper hence marks the first effort in the computer vision community to jointly consider various types of actors undergoing various actions. To start with the problem, we collect a dataset of 3782 videos from YouTube and label both pixel-level actors and actions in each video. We formulate the general actor-action understanding problem and instantiate it at various granularities: both video-level single- and multiple-label actor-action recognition and pixel-level actor-action semantic segmentation. Our experiments demonstrate that inference jointly over actors and actions outperforms inference independently over them, and hence concludes our argument of the value of explicit consideration of various actors in comprehensive action understanding.

@InProceedings{Xu_2015_CVPR,author = {Xu, Chenliang and Hsieh, Shao-Hang and Xiong, Caiming and Corso, Jason J.},title = {Can Humans Fly? Action Understanding With Multiple Classes of Actors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Lei Zhang, Wei Wei, Yanning Zhang, Chunna Tian, Fei Li

Abstract: Compressive sensing(CS) has been exploited for hypespectral image(HSI) compression in recent years. Though it can greatly reduce the costs of computation and storage, the reconstruction of HSI from a few linear measurements is challenging. The underlying sparsity of HSI is crucial to improve the reconstruction accuracy. However, the sparsity of HSI is unknown in reality and varied with different noise, which makes the sparsity estimation difficult. To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study. First, the reweighted Laplace prior is proposed to model the distribution of sparsity in HSI. Second, the latent variable Bayes model is employed to learn the optimal configuration of the reweighted Laplace prior from the measurements. The model unifies signal recovery, prior learning and noise estimation into a variational framework to infer the parameters automatically. The learned sparsity prior can represent the underlying structure of the sparse signal very well and is adaptive to the unknown noise, which improves the reconstruction accuracy of HSI. The experimental results on three hyperspectral datasets demonstrate the proposed method outperforms serveral state-of-the-art hyperspectral CS methods on the reconstruction accuracy.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Lei and Wei, Wei and Zhang, Yanning and Tian, Chunna and Li, Fei},title = {Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ashish Shrivastava, Mohammad Rastegari, Sumit Shekhar, Rama Chellappa, Larry S. Davis

Abstract: Many existing recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time. We describe an algorithm that perturbs test features so that all modalities predict the same class. We enforce this perturbation to be as small as possible via a quadratic program (QP) for continuous features, and a mixed integer program (MIP) for binary features. To efficiently solve the MIP, we provide a greedy algorithm and empirically show that its solution is very close to that of a state-of-the-art MIP solver. We evaluate our algorithm on several datasets and show that the method outperforms existing approaches.

@InProceedings{Shrivastava_2015_CVPR,author = {Shrivastava, Ashish and Rastegari, Mohammad and Shekhar, Sumit and Chellappa, Rama and Davis, Larry S.},title = {Class Consistent Multi-Modal Fusion With Binary Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Cenek Albl, Zuzana Kukelova, Tomas Pajdla

Abstract: We present a minimal, non-iterative solution to the absolute pose problem for images from rolling shutter cameras. Absolute pose problem is a key problem in computer vision and rolling shutter is present in a vast majority of today's digital cameras. We propose several rolling shutter camera models and verify their feasibility for a polynomial solver. A solution based on linearized camera model is chosen and verified in several experiments. We use a linear approximation to the camera orientation, which is meaningful only around the identity rotation. We show that the standard P3P algorithm is able to estimate camera orientation within 6 degrees for camera rotation velocity as high as 30deg/frame. Therefore we can use the standard P3P algorithm to estimate camera orientation and to bring the camera rotation matrix close to the identity. Using this solution, camera position, orientation, translational velocity and angular velocity can be computed using six 2D-to-3D correspondences, with orientation error under half a degree and relative position error under 2%. A significant improvement in terms of the number of inliers in RANSAC is demonstrated.

@InProceedings{Albl_2015_CVPR,author = {Albl, Cenek and Kukelova, Zuzana and Pajdla, Tomas},title = {R6P - Rolling Shutter Absolute Camera Pose},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Daniel Moreno, Kilho Son, Gabriel Taubin

Abstract: We introduce Embedded PS, a new robust and accurate phase shifting algorithm for 3D scanning. The method projects only high frequency sinusoidal patterns in order to reduce errors due to global illumination effects, such as subsurface scattering and interreflections. The frequency set for the projected patterns is specially designed so that our algorithm can extract a set of embedded low frequency sinusoidals with simple math. All the signals, patterns high and embedded low frequencies, are used with temporal phase unwrapping to compute absolute phase values in closed-form, without quantization or approximation via LUT, resulting in fast computation. The absolute phases provide correspondences from projector to camera pixels which enable to recover 3D points using optical triangulation. The algorithm estimates multiple absolute phase values per pixel which are combined to reduce measurement noise while preserving fine details. We prove that embedded periodic signals can be recovered from any periodic signal, not just sinusoidal signals, which may result in further improvements for other 3D imaging methods. Several experiments are presented showing that our algorithm produces more robust and accurate 3D scanning results than state-of-the-art methods for challenging surface materials, with an equal or smaller number of projected patterns and at lower computational cost.

@InProceedings{Moreno_2015_CVPR,author = {Moreno, Daniel and Son, Kilho and Taubin, Gabriel},title = {Embedded Phase Shifting: Robust Phase Shifting With Embedded Signals},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Trung Ngo Thanh, Hajime Nagahara, Rin-ichiro Taniguchi

Abstract: We introduce a method to recover the shape of a smooth dielectric object from polarization images taken with a light source from different directions. We present two constraints on shading and polarization and use both in a single optimization scheme. This integration is motivated by the fact that photometric stereo and polarization-based methods have complementary abilities. The polarization-based method can give strong cues for the surface orientation and refractive index, which are independent of the light direction. However, it has ambiguities in selecting between two ambiguous choices of the surface orientation, in the relationship between refractive index and zenith angle (observing angle), and limited performance for surface points with small zenith angles, where the polarization effect is weak. In contrast, photometric stereo method with multiple light sources can disambiguate the surface orientation and give a strong relationship between the surface normals and light directions. However, it has limited performance for large zenith angles, refractive index estimation, and faces the ambiguity in case the light direction is unknown. Taking their advantages, our proposed method can recover the surface normals for both small and large zenith angles, the light directions, and the refractive indexes of the object. The proposed method is successfully evaluated by simulation and real-world experiments.

@InProceedings{Thanh_2015_CVPR,author = {Ngo Thanh, Trung and Nagahara, Hajime and Taniguchi, Rin-ichiro},title = {Shape and Light Directions From Shading and Polarization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


3D Deep Shape Descriptor

Yi Fang, Jin Xie, Guoxian Dai, Meng Wang, Fan Zhu, Tiantian Xu, Edward Wong

Abstract: Shape descriptor is a concise yet informative representation that provides a 3D object with an identification as a member of some category. This paper developed a concise deep shape descriptor for the first time to address challenging issues from ever-growing 3D datasets in areas as diverse as engineering, medicine, and biology. Specifically, the proposed approach developed novel techniques to extract concise but geometrically informative shape descriptor, new definitions of Eigen-shape descriptor and Fisher-shape descriptor to guide the training strategy for deep neural network, and deep shape descriptor with discriminative capacity of maximizing the inter-class margin while minimizing the intra-class variance. Our approach addressed the challenges for shape analysis techniques posed by the complexity of 3D model and data representation and geometric structural variations and noise present in 3D models. The experimental results on 3D shape retrieval demonstrate that our proposed deep shape descriptor is superior to other state-of-the-art approaches on handling noise, incompleteness and 3D shape structural variations.

@InProceedings{Fang_2015_CVPR,author = {Fang, Yi and Xie, Jin and Dai, Guoxian and Wang, Meng and Zhu, Fan and Xu, Tiantian and Wong, Edward},title = {3D Deep Shape Descriptor},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper we present a novel framework for cross-age face verification (FV) by seeking help from its ``competitor" named cross-face age verification (AV), i.e., deciding whether two face photos are taken at similar ages. While FV and AV share some common features, FV pursues age insensitivity and AV seeks age sensitivity.  Such correlation suggests that AV may be used to guide feature selection in FV, i.e., by reducing the chance of choosing age sensitive features.    Driven by this intuition, we propose to learn a solution for cross-age face verification by coordinating with a solution for age verification. Specifically, a joint additive model is devised to simultaneously handling both tasks, while encoding feature coordination by a competition regularization term.    Then, an alternating greedy coordinate descent (AGCD) algorithm is developed to solve this joint model.  As shown in our experiments, the algorithm effectively balances feature sharing and feature exclusion between the two tasks; and, for face verification, the algorithm effectively removes distracting features used in age verification.    To evaluate the proposed algorithm, we conduct cross-age face verification experiments using two benchmark cross-age face datasets, FG-Net and MORPH. In all experiments, our algorithm achieves very promising results and outperforms all previously tested solutions.

@InProceedings{Du_2015_CVPR,author = {Du, Liang and Ling, Haibin},title = {Cross-Age Face Verification by Coordinating With Cross-Face Age Verification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Cayley-Klein metric is a kind of non-Euclidean metric suitable for projective space. In this paper, we introduce it into the computer vision community as a powerful metric and an alternative to the widely studied Mahalanobis metric. We show that besides its good characteristic in non-Euclidean space, it is a generalization of Mahalanobis metric in some specific cases. Furthermore, as many Mahalanobis metric learning, we give two kinds of Cayley-Klein metric learning methods: MMC Cayley-Klein metric learning and LMNN Cayley-Klein metric learning. Experiments have shown the superiority of Cayley-Klein metric over Mahalanobis ones and the effectiveness of our Cayley-Klein metric learning methods.

@InProceedings{Bi_2015_CVPR,author = {Bi, Yanhong and Fan, Bin and Wu, Fuchao},title = {Beyond Mahalanobis Metric: Cayley-Klein Metric Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: The locality-constrained linear coding (LLC) is a very successful feature coding method in image classification. It makes known the importance of locality constraint which brings high efficiency and local smoothness of the codes. However, in the LLC method the geometry of feature space is described by an ensemble of representative points (visual words) while discarding the geometric structure immediately surrounding them. Such a dictionary only provides a crude, piecewise constant approximation of the data manifold. To approach this problem, we propose a novel feature coding method called locality-constrained affine subspace coding (LASC). The data manifold in LASC is characterized by an ensemble of subspaces attached to the representative points (or affine subspaces), which can provide a piecewise linear approximation of the manifold. Given an input descriptor, we find its top-k neighboring subspaces, in which the descriptor is linearly decomposed and weighted to form the first-order LASC vector. Inspired by the success of usage of higher-order information in image classification, we propose the second-order LASC vector based on the Fisher information metric for further performance improvement. We make experiments on challenging benchmarks and experiments have shown the LASC method is very competitive.

@InProceedings{Li_2015_CVPR,author = {Li, Peihua and Lu, Xiaoxiao and Wang, Qilong},title = {From Dictionary of Visual Words to Subspaces: Locality-Constrained Affine Subspace Coding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Lei Xiao, Felix Heide, Matthew O'Toole, Andreas Kolb, Matthias B. Hullin, Kyros Kutulakos, Wolfgang Heidrich

Abstract: Continuous-wave time-of-flight (ToF) cameras show great promise as low-cost depth image sensors in mobile applications. However, they also suffer from several challenges, including limited illumination intensity, which mandates the use of large numerical aperture lenses, and thus results in a shallow depth of field, making it difficult to capture scenes with large variations in depth. Another shortcoming is the limited spatial resolution of currently available ToF sensors.  In this paper we analyze the image formation model for blurred ToF images. By directly working with raw sensor measurements but regularizing the recovered depth and amplitude images, we are able to simultaneously deblur and super-resolve the output of ToF cameras. Our method outperforms existing methods on both synthetic and real datasets. In the future our algorithm should extend easily to cameras that do not follow the cosine model of continuous-wave sensors, as well as to multi-frequency or multi-phase imaging employed in more recent ToF cameras.

@InProceedings{Xiao_2015_CVPR,author = {Xiao, Lei and Heide, Felix and O'Toole, Matthew and Kolb, Andreas and Hullin, Matthias B. and Kutulakos, Kyros and Heidrich, Wolfgang},title = {Defocus Deblurring and Superresolution for Time-of-Flight Depth Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Vassileios Balntas, Lilian Tang, Krystian Mikolajczyk

Abstract: In this paper we propose a novel approach to generate a binary descriptor optimized for each image patch independently.    The approach is inspired by the linear discriminant embedding that simultaneously increases inter and decreases intra class distances. A set of discriminative and uncorrelated binary tests is established from all possible tests in an offline training process. The patch adapted descriptors are then efficiently built online from a subset of tests which lead to lower intra class distances thus a more robust descriptor. A patch descriptor consists of two binary strings where one represents the results of the tests and the other indicates the subset of the patch-related robust tests that are used for calculating a masked Hamming distance.  Our experiments on three different benchmarks demonstrate improvements in  matching performance, and illustrate that per-patch optimization outperforms global optimization.

@InProceedings{Balntas_2015_CVPR,author = {Balntas, Vassileios and Tang, Lilian and Mikolajczyk, Krystian},title = {BOLD - Binary Online Learned Descriptor For Efficient Image Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Huaijin Chen, M. Salman Asif, Aswin C. Sankaranarayanan, Ashok Veeraraghavan

Abstract: Cameras for imaging in short and mid-wave infrared spectra are significantly more expensive than their counterparts in visible imaging. As a result, high-resolution imaging in those spectrum remains beyond the reach of most consumers. Over the last decade, compressive sensing (CS) has emerged as a potential means to realize inexpensive short-wave infrared cameras. One approach for doing this is the single-pixel camera (SPC) where a single detector acquires coded measurements of a high-resolution image. A computational reconstruction algorithm is then used to recover the image from these coded measurements. Unfortunately, the measurement rate of a SPC is insufficient to enable imaging at high spatial and temporal resolutions. We present a focal plane array-based compressive sensing (FPA-CS) architecture that achieves high spatial and temporal resolutions. The idea is to use an array of SPCs that sense in parallel to increase the measurement rate, and consequently, the achievable spatio-temporal resolution of the camera. We develop a proof-of-concept prototype in the short-wave infrared using a sensor with 64 x 64 pixels; the prototype provides a 4096x increase in the measurement rate compared to the SPC and achieves a megapixel resolution at video rate using CS techniques.

@InProceedings{Chen_2015_CVPR,author = {Chen, Huaijin and Salman Asif, M. and Sankaranarayanan, Aswin C. and Veeraraghavan, Ashok},title = {FPA-CS: Focal Plane Array-Based Compressive Imaging in Short-Wave Infrared},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Numerous recent approaches attempt to remove image blur due to camera shake,  either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually  all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent  inverse problem.  The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method's rationale is that camera shake has a random nature and therefore  each image in the burst is generally blurred differently.  Experiments with real camera data show that the proposed Fourier Burst Accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones.

@InProceedings{Delbracio_2015_CVPR,author = {Delbracio, Mauricio and Sapiro, Guillermo},title = {Burst Deblurring: Removing Camera Shake Through Fourier Burst Accumulation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Peng Zhang, Wengang Zhou, Lei Wu, Houqiang Li

Abstract: Image quality assessment (IQA) tries to estimate human perception based image visual quality in an objective manner.  Existing approaches target this problem with or without reference images.  For no-reference image quality assessment, there is no given reference image or any knowledge of the distortion type of the image.  Previous approaches measure the image quality from signal level rather than semantic analysis.  They typically depend on various features to represent local characteristic of an image.  In this paper we propose a new no-reference (NR) image quality assessment (IQA) framework based on semantic obviousness.  We discover that semantic-level factors affect human perception of image quality. With such observation, we explore semantic obviousness as a metric to perceive objects of an image. We propose to extract two types of features, one to measure the semantic obviousness of the image and the other to discover local characteristic.  Then the two kinds of features are combined for image quality estimation. The principles proposed in our approach can also be incorporated with many existing IQA algorithms to boost their performance.  We evaluate our approach on the LIVE dataset. Our approach is demonstrated to be superior to the existing NR-IQA algorithms and comparable to the state-of-the-art full-reference IQA (FR-IQA) methods. Cross-dataset experiments show the generalization ability of our approach.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Peng and Zhou, Wengang and Wu, Lei and Li, Houqiang},title = {SOM: Semantic Obviousness Metric for Image Quality Assessment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen-Change Loy, Xiaoou Tang

Abstract: In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection diagram has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN, which is the state-of-the-art, from $31\%$ to $50.3\%$ on the ILSVRC2014 detection dataset. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provide a global view for people to understand the deep learning object detection pipeline.

@InProceedings{Ouyang_2015_CVPR,author = {Ouyang, Wanli and Wang, Xiaogang and Zeng, Xingyu and Qiu, Shi and Luo, Ping and Tian, Yonglong and Li, Hongsheng and Yang, Shuo and Wang, Zhe and Loy, Chen-Change and Tang, Xiaoou},title = {DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tat-Jun Chin, Pulak Purkait, Anders Eriksson, David Suter

Abstract: Maximum consensus is one of the most popular criteria for robust estimation in computer vision. Despite its widespread use, optimising the criterion is still customarily done by randomised sample-and-test techniques, which do not guarantee optimality of the result. Several globally optimal algorithms exist, but they are too slow to challenge the dominance of randomised methods. We aim to change this state of affairs by proposing a very efficient algorithm for global maximisation of consensus. Under the framework of LP-type methods, we show how consensus maximisation for a wide variety of vision tasks can be posed as a tree search problem. This insight leads to a novel algorithm based on A* search. We propose efficient heuristic and support set updating routines that enable A* search to rapidly find globally optimal results. On common estimation problems, our algorithm is several orders of magnitude faster than previous exact methods. Our work identifies a promising solution for globally optimal consensus maximisation.

@InProceedings{Chin_2015_CVPR,author = {Chin, Tat-Jun and Purkait, Pulak and Eriksson, Anders and Suter, David},title = {Efficient Globally Optimal Consensus Maximisation With Tree Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We address the problem of estimating location information of an image using principles from automated representation learning. We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations, by initializing it with a geometric prior corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of parallel transport on manifolds. We then extend this approach to account for the availability of heterogeneous data modalities such as geo-tags and videos pertaining to different locations, and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations. We evaluate our approach on several standard datasets such as im2gps, San Francisco and MediaEval2010, and obtain state-of-the-art results.

@InProceedings{Gopalan_2015_CVPR,author = {Gopalan, Raghuraman},title = {Hierarchical Sparse Coding With Geometric Prior For Visual Geo-Location},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description.  We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans $21.0\%$ of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.

@InProceedings{Chen_2015_CVPR,author = {Chen, Xinlei and Lawrence Zitnick, C.},title = {Mind's Eye: A Recurrent Visual Representation for Image Caption Generation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: It is well known that the problem of camera pose estimation with unknown focal length has 7 degrees of freedom. Since each image point gives 2 constraints, solving this problem requires a minimum of 3.5 image points of 4 known 3D points, where 0.5 means either x or y coordinate of an image point.  We refer to this minimal problem as P3.5P. However, the existing methods require 4 full image points to solve the camera pose and  focal length.  In this paper, we present a general solution to the true minimal P3.5P problem with up to 10 solutions.  The remaining image coordinate is then used to filter the candidate solutions, which typically results in  a single solution for good data or no solution for outliers. Experiments show the proposed method significantly improves the efficiency over the state of the art methods while maintaining a high accuracy.

@InProceedings{Wu_2015_CVPR,author = {Wu, Changchang},title = {P3.5P: Pose Estimation With Unknown Focal Length},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We present a novel vanishing point (VP) detection and tracking algorithm for calibrated monocular image sequences.   Previous VP detection and tracking methods usually assume known camera poses for all frames or detect and track separately. We advance the state-of-the-art by combining VP extraction on a Gaussian sphere with recent advances in multi-target tracking on probabilistic occupancy fields.  The solution is obtained by solving a Linear Program (LP). This enables the joint detection and tracking of multiple VPs over sequences. Unlike existing works we do not need known camera poses, and at the same time avoid detecting and tracking in separate steps. We also propose an extension to enforce VP orthogonality. We augment an existing video dataset consisting of 48 monocular videos with multiple annotated VPs in 14448 frames for evaluation. Although the method is designed for unknown camera poses, it is also helpful in scenarios with known poses, since a multi-frame approach in VP detection helps to regularize in frames with weak VP line support.

@InProceedings{Kroeger_2015_CVPR,author = {Kroeger, Till and Dai, Dengxin and Van Gool, Luc},title = {Joint Vanishing Point Extraction and Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that  transfers knowledge from multiple views to a canonical view. The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view. The strength of our technique is that we learn a single NKTM for all actions and all camera viewing directions. Thus, NKTM does not require action labels during learning and knowledge of the camera viewpoints during training or testing. NKTM is learned once only from dense trajectories of synthetic points fitted to mocap data and then applied to real video data. Trajectories are coded with a general codebook learned from the same mocap data. NKTM is scalable to new action classes and training data as it does not require re-learning. Experiments on the IXMAS and N-UCLA datasets show that NKTM outperforms existing state-of-the-art methods for cross-view action recognition.

@InProceedings{Rahmani_2015_CVPR,author = {Rahmani, Hossein and Mian, Ajmal},title = {Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ho Yub Jung, Soochahn Lee, Yong Seok Heo, Il Dong Yun

Abstract: The availability of accurate depth cameras have made real-time human pose estimation possible; however, there are still demands for faster algorithms on low power processors. This paper introduces 1000 frames per second pose estimation method on a single core CPU. A large computation gain is achieved by random walk sub-sampling. Instead of training trees for pixel-wise classification, a regression tree is trained to estimate the probability distribution to the direction toward the particular joint, relative to the current position. At test time, the direction for the random walk is randomly chosen from a set of representative directions. The new position is found by a constant step toward the direction, and the distribution for next direction is found at the new position. The continual random walk through 3D space will eventually produce an expectation of step positions, which we estimate as the joint position. A regression tree is built separately for each joint. The number of random walk steps can be assigned for each joint so that the computation time is consistent regardless of the size of body segmentation. The experiments show that even with large computation gain, the accuracy is higher or comparable to the state-of-the-art pose estimation methods.

@InProceedings{Jung_2015_CVPR,author = {Yub Jung, Ho and Lee, Soochahn and Seok Heo, Yong and Dong Yun, Il},title = {Random Tree Walk Toward Instantaneous 3D Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, Jie Zhou

Abstract: In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the deep network: 1) the loss between the original real-valued feature descriptor and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) by including one discriminative term into the objective function of DH which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes. Experimental results show the superiority of the proposed approach over the state-of-the-arts.

@InProceedings{Liong_2015_CVPR,author = {Erin Liong, Venice and Lu, Jiwen and Wang, Gang and Moulin, Pierre and Zhou, Jie},title = {Deep Hashing for Compact Binary Codes Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jason Rock, Tanmay Gupta, Justin Thorsen, JunYoung Gwak, Daeyun Shin, Derek Hoiem

Abstract: Our goal is to recover a complete 3D model from a depth image of an object. Existing approaches rely on user interaction or apply to a limited class of objects, such as chairs. We aim to fully automatically reconstruct a 3D model from any category. We take an exemplar-based approach: retrieve similar objects in a database of 3D models using view-based matching and transfer the symmetries and surfaces from retrieved models. We investigate completion of 3D models in three cases: novel view (model in database); novel model (models for other objects of the same category in database); and novel category (no models from the category in database).

@InProceedings{Rock_2015_CVPR,author = {Rock, Jason and Gupta, Tanmay and Thorsen, Justin and Gwak, JunYoung and Shin, Daeyun and Hoiem, Derek},title = {Completing 3D Object Shape From One Depth Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Thomas Mauthner, Horst Possegger, Georg Waltner, Horst Bischof

Abstract: We present a novel video saliency detection method to support human activity recognition and weakly supervised training of activity detection algorithms. Recent research has emphasized the need for analyzing salient information in videos to minimize dataset bias or to supervise weakly labeled training of activity detectors. In contrast to previous methods we do not rely on training information given by either eye-gaze or annotation data, but propose a fully unsupervised algorithm to find salient regions within videos. In general, we enforce the Gestalt principle of figure-ground segregation for both appearance and motion cues. We introduce an encoding approach that allows for efficient computation of saliency by approximating joint feature distributions. We evaluate our approach on several datasets, including challenging scenarios with cluttered background and camera motion, as well as salient object detection in images. Overall, we demonstrate favorable performance compared to state-of-the-art methods in estimating both ground-truth eye-gaze and activity annotations.

@InProceedings{Mauthner_2015_CVPR,author = {Mauthner, Thomas and Possegger, Horst and Waltner, Georg and Bischof, Horst},title = {Encoding Based Saliency Detection for Videos and Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Online Sketching Hashing

Cong Leng, Jiaxiang Wu, Jian Cheng, Xiao Bai, Hanqing Lu

Abstract: Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.

@InProceedings{Leng_2015_CVPR,author = {Leng, Cong and Wu, Jiaxiang and Cheng, Jian and Bai, Xiao and Lu, Hanqing},title = {Online Sketching Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Christopher Bongsoo Choy, Michael Stark, Sam Corbett-Davies, Silvio Savarese

Abstract: A large body of recent work on object detection has focused on exploiting 3D CAD model databases to improve detection performance. Many of these approaches work by aligning exact 3D models to images using templates generated from renderings of the 3D models at a set of discrete viewpoints. However, the training procedures for these approaches are computationally expensive and require gigabytes of memory and storage, while the viewpoint discretization hampers pose estimation performance.  We propose an efficient method for synthesizing templates from 3D models that runs on the fly -- that is, it quickly produces detectors for an arbitrary viewpoint of a 3D model without expensive dataset-dependent training or template storage. Given a 3D model and an arbitrary continuous detection viewpoint, our method synthesizes a discriminative template by extracting features from a rendered view of the object and decorrelating spatial dependences among the features. Our decorrelation procedure relies on a gradient-based algorithm that is more numerically stable than standard decomposition-based procedures, and we efficiently search for candidate detections by computing FFT-based template convolutions. Due to the speed of our template synthesis procedure, we are able to perform joint optimization of scale, translation, continuous rotation, and focal length using Metropolis-Hastings algorithm. We provide an efficient GPU implementation of our algorithm, and we validate its performance on 3D Object Classes and PASCAL3D+ datasets.

@InProceedings{Choy_2015_CVPR,author = {Bongsoo Choy, Christopher and Stark, Michael and Corbett-Davies, Sam and Savarese, Silvio},title = {Enriching Object Detection With 2D-3D Registration and Continuous Viewpoint Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}