Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka

Abstract: Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision. Generative probabilistic models, or analysis-by-synthesis approaches, can capture rich scene structure but have been less widely applied than their discriminative counterparts, as they often require considerable problem-specific engineering in modeling and inference, and inference is typically seen as requiring slow, hypothesize-and-test Monte Carlo methods.  Here we present Picture, a probabilistic programming language for scene understanding that allows researchers to express complex generative vision models, while automatically solving them using fast general-purpose inference machinery. Picture provides a stochastic scene language that can express generative models for arbitrary 2D/3D scenes, as well as a hierarchy of representation layers for comparing scene hypotheses with observed images by matching not simply pixels, but also more abstract features (e.g., contours, deep neural network activations). Inference can flexibly integrate advanced Monte Carlo strategies with fast bottom-up data-driven methods. Thus both representations and inference strategies can build directly on progress in discriminatively trained systems to make generative vision more robust and efficient. We use Picture to write programs for 3D face analysis, 3D human pose estimation, and 3D object reconstruction - each competitive with specially engineered baselines.

@InProceedings{Kulkarni_2015_CVPR,author = {Kulkarni, Tejas D. and Kohli, Pushmeet and Tenenbaum, Joshua B. and Mansinghka, Vikash},title = {Picture: A Probabilistic Programming Language for Scene Perception},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Julien Valentin, Matthias Niessner, Jamie Shotton, Andrew Fitzgibbon, Shahram Izadi, Philip H. S. Torr

Abstract: Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure. In these methods, each tree associates one pixel with a point in the scene's 3D world coordinate frame. In previous work, these predictions were point estimates and the subsequent camera pose optimization implicitly assumed an isotropic distribution of these estimates. In this paper, we train a regression forest to predict mixtures of anisotropic 3D Gaussians and show how the predicted uncertainties can be taken into account for continuous pose optimization. Experiments show that our proposed method is able to relocalize up to 40% more frames than the state-of-the-art.

@InProceedings{Valentin_2015_CVPR,author = {Valentin, Julien and Niessner, Matthias and Shotton, Jamie and Fitzgibbon, Andrew and Izadi, Shahram and Torr, Philip H. S.},title = {Exploiting Uncertainty in Regression Forests for Accurate Camera Relocalization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Video event recognition still faces great challenges due to large intra-class variation and low image resolution, in particular for surveillance videos. To mitigate these challenges and to improve the event recognition performance, various context information from the feature level, the semantic level, as well as the prior level is utilized. Different from most existing context approaches that utilize context in one of the three levels through shallow models like support vector machines, or probabilistic models like BN and MRF, we propose a deep hierarchical context model that simultaneously learns and integrates context at all three levels, and holistically utilizes the integrated contexts for event recognition. We first introduce two types of context features describing the event neighborhood, and then utilize the proposed deep model to learn the middle level representations and combine the bottom feature level, middle semantic level and top prior level contexts together for event recognition. The experiments on state of art surveillance video event benchmarks including VIRAT 1.0 Ground Dataset, VIRAT 2.0 Ground Dataset, and the UT-Interaction Dataset demonstrate that the proposed model is quite effective in utilizing the context information for event recognition. It outperforms the existing context approaches that also utilize multiple level contexts on these event benchmarks.

@InProceedings{Wang_2015_CVPR,author = {Wang, Xiaoyang and Ji, Qiang},title = {Video Event Recognition With Deep Hierarchical Context Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Huazhu Fu, Dong Xu, Stephen Lin, Jiang Liu

Abstract: We present an object-based co-segmentation method that takes advantage of depth data and is able to correctly handle noisy images in which the common foreground object is missing. With RGBD images, our method utilizes the depth channel to enhance identification of similar foreground objects via a proposed RGBD co-saliency map, as well as to improve detection of object-like regions and provide depth-based local features for region comparison. To accurately deal with noisy images where the common object appears more than or less than once, we formulate co-segmentation in a fully-connected graph structure together with mutual exclusion (mutex) constraints that prevent improper solutions. Experiments show that this object-based RGBD co-segmentation with mutex constraints outperforms related techniques on an RGBD co-segmentation dataset, while effectively processing noisy images. Moreover, we show that this method also provides performance comparable to state-of-the-art RGB co-segmentation techniques on regular RGB images with depth maps estimated from them.

@InProceedings{Fu_2015_CVPR,author = {Fu, Huazhu and Xu, Dong and Lin, Stephen and Liu, Jiang},title = {Object-Based RGBD Image Co-Segmentation With Mutex Constraint},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yang Song, Weidong Cai, Qing Li, Fan Zhang, David Dagan Feng, Heng Huang

Abstract: Texture, as a fundamental characteristic of objects, has attracted much attention in computer vision research. Performance of texture classification is however still lacking for some challenging cases, largely due to the high intra-class variation and low inter-class distinction. To tackle these issues, in this paper, we propose a sub-categorization model for texture classification. By clustering each class into subcategories, classification probabilities at the subcategory-level are computed based on between-subcategory distinctiveness and within-subcategory representativeness. These subcategory probabilities are then fused based on their contribution levels and cluster qualities. This fused probability is added to the multiclass classification probability to obtain the final class label. Our method was applied to texture classification on three challenging datasets - KTH-TIPS2, FMD and DTD, and has shown excellent performance in comparison with the state-of-the-art approaches.

@InProceedings{Song_2015_CVPR,author = {Song, Yang and Cai, Weidong and Li, Qing and Zhang, Fan and Dagan Feng, David and Huang, Heng},title = {Fusing Subcategory Probabilities for Texture Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Benjamin Klein, Guy Lev, Gil Sadeh, Lior Wolf

Abstract: In recent years, the problem of associating a sentence with an image has gained a lot of attention. This work continues to push the envelope and makes further progress in the performance of image annotation and image search by a sentence tasks. In this work, we are using the Fisher Vector as a sentence representation by pooling the word2vec embedding of each word in the sentence. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). In this work we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first  is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. Finally, by using the new Fisher Vectors derived from HGLMMs to represent sentences, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks on four benchmarks: Pascal1K, Flickr8K, Flickr30K, and COCO.

@InProceedings{Klein_2015_CVPR,author = {Klein, Benjamin and Lev, Guy and Sadeh, Gil and Wolf, Lior},title = {Associating Neural Word Embeddings With Deep Image Representations Using Fisher Vectors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiaowei Zhou, Spyridon Leonardos, Xiaoyan Hu, Kostas Daniilidis

Abstract: We investigate the problem of estimating the 3D shape of an object, given a set of 2D landmarks in a single image. To alleviate the reconstruction ambiguity, a widely-used approach is to confine the unknown 3D shape within a shape space built upon existing shapes. While this approach has proven to be successful in various applications, a challenging issue remains, i.e., the joint estimation of shape parameters and camera-pose parameters requires to solve a nonconvex optimization problem. The existing methods often adopt an alternating minimization scheme to locally update the parameters, and consequently the solution is sensitive to initialization. In this paper, we propose a convex formulation to address this problem and develop an efficient algorithm to solve the proposed convex program. We demonstrate the exact recovery property of the proposed method, its merits compared to alternative methods, and the applicability in human pose and car shape estimation.

@InProceedings{Zhou_2015_CVPR,author = {Zhou, Xiaowei and Leonardos, Spyridon and Hu, Xiaoyan and Daniilidis, Kostas},title = {3D Shape Estimation From 2D Landmarks: A Convex Relaxation Approach},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Andelo Martinovic, Jan Knopp, Hayko Riemenschneider, Luc Van Gool

Abstract: We propose a new approach for semantic segmentation of 3D city models. Starting from an SfM reconstruction of a street-side scene, we perform classification and facade splitting purely in 3D, obviating the need for slow image-based semantic segmentation methods. We show that a properly trained pure-3D approach produces high quality labelings, with significant speed benefits (20x faster) allowing us to analyze entire streets in a matter of minutes. Additionally, if speed is not of the essence, the 3D labeling can be combined with the results of a state-of-the-art 2D classifier, further boosting the performance. Further, we propose a novel facade separation based on semantic nuances between facades. Finally, inspired by the use of architectural principles for 2D facade labeling, we propose new 3D-specific principles and an efficient optimization scheme based on an integer quadratic programming formulation.

@InProceedings{Martinovic_2015_CVPR,author = {Martinovic, Andelo and Knopp, Jan and Riemenschneider, Hayko and Van Gool, Luc},title = {3D All The Way: Semantic Segmentation of Urban Scenes From Start to End in 3D},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jonathan T. Barron, Andrew Adams, YiChang Shih, Carlos Hernandez

Abstract: Given a stereo pair it is possible to recover a depth map and use that depth to render a synthetically defocused image. Though stereo algorithms are well-studied, rarely are those algorithms considered solely in the context of producing these defocused renderings. In this paper we present a technique for efficiently producing disparity maps using a novel optimization framework in which inference is performed in "bilateral-space". Our approach produces higher-quality "defocus" results than other stereo algorithms while also being 10-100 times faster than comparable techniques.

@InProceedings{Barron_2015_CVPR,author = {Barron, Jonathan T. and Adams, Andrew and Shih, YiChang and Hernandez, Carlos},title = {Fast Bilateral-Space Stereo for Synthetic Defocus},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Nicola Fioraio, Jonathan Taylor, Andrew Fitzgibbon, Luigi Di Stefano, Shahram Izadi

Abstract: Depth cameras have helped commoditize 3D digitization of the real-world. It is now feasible to use a single Kinect-like camera to scan in an entire building or other large-scale scenes. At large scales, however, there is an inherent challenge of dealing with distortions and drift due to accumulated pose estimation errors. Existing techniques suffer from one or more of the following: a) requiring an expensive offline global optimization step taking hours to compute; b) needing a full second pass over the input depth frames to correct for accumulated errors; c) relying on RGB data alongside depth data to optimize poses; or d) requiring the user to create explicit loop closures to allow gross alignment errors to be resolved. In this paper, we present a method that addresses all of these issues. Our method supports online model correction, without needing to reprocess or store any input depth data. Even while performing global correction of a large 3D model, our method takes only minutes rather than hours to compute. Our model does not require any explicit loop closures to be detected and, finally, relies on depth data alone, allowing operation in low-lighting conditions. We show qualitative results on many large scale scenes, highlighting the lack of error and drift in our reconstructions. We compare to state of the art techniques and demonstrate large-scale dense surface reconstruction "in the dark", a capability not offered by RGB-D techniques.

@InProceedings{Fioraio_2015_CVPR,author = {Fioraio, Nicola and Taylor, Jonathan and Fitzgibbon, Andrew and Di Stefano, Luigi and Izadi, Shahram},title = {Large-Scale and Drift-Free Surface Reconstruction Using Online Subvolume Registration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tae-Hyun Oh, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon

Abstract: Rank minimization problem can be boiled down to either Nuclear Norm Minimization (NNM) or Weighted NNM (WNNM) problem. The problems related to NNM (or WNNM) can be solved iteratively by applying a closed-form proximal operator, called Singular Value Thresholding (SVT) (or Weighted SVT), but they suffer from high computational cost to compute a Singular Value Decomposition (SVD) at each iteration. In this paper, we propose an accurate and fast approximation method for SVT, called fast randomized SVT (FRSVT), where we avoid direct computation of SVD. The key idea is to extract an approximate basis for the range of a matrix from its compressed matrix. Given the basis, we compute the partial singular values of the original matrix from a small factored matrix. While the basis approximation is the bottleneck, our method is already severalfold faster than thin SVD. By adopting a range propagation technique, we can further avoid one of the bottleneck at each iteration. Our theoretical analysis provides a stepping stone between the approximation bound of SVD and its effect to NNM via SVT. Along with the analysis, our empirical results on both quantitative and qualitative studies show our approximation rarely harms the convergence behavior of the host algorithms. We apply it and validate the efficiency of our method on various vision problems, e.g. subspace clustering, weather artifact removal, simultaneous multi-image alignment and rectification.

@InProceedings{Oh_2015_CVPR,author = {Oh, Tae-Hyun and Matsushita, Yasuyuki and Tai, Yu-Wing and So Kweon, In},title = {Fast Randomized Singular Value Thresholding for Nuclear Norm Minimization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Danda Pani Paudel, Adlane Habed, Cedric Demonceaux, Pascal Vasseur

Abstract: This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates, and two or more uncalibrated cameras. An unknown subset of the scanned points have their image projections detected and matched across images. The proposed approach assumes the cameras only known in some arbitrary projective frame and no calibration or autocalibration is required. The devised solution is based on a Linear Matrix Inequality (LMI) framework that allows simultaneously estimating the projective transformation relating the cameras to the scene and establishing 2D-3D correspondences without triangulating image points. The proposed LMI framework allows both deriving triangulation-free LMI cheirality conditions and establishing putative correspondences between 3D volumes (boxes) and 2D pixel coordinates. Two registration algorithms, one exploiting the scene's structure and the other concerned with robustness, are presented. Both algorithms employ the Branch-and-Prune paradigm and guarantee convergence to a global solution under mild initial bound conditions. The results of our experiments are presented and compared against other approaches.

@InProceedings{Paudel_2015_CVPR,author = {Pani Paudel, Danda and Habed, Adlane and Demonceaux, Cedric and Vasseur, Pascal},title = {LMI-Based 2D-3D Registration: From Uncalibrated Images to Euclidean Scene},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wei-Zhi Nie, An-An Liu, Zan Gao, Yu-Ting Su

Abstract: This paper originally proposes the clique-graph and further presents a clique-graph matching method by preserving global and local structures. Especially, we formulate the objective function of clique-graph matching with respective to two latent variables, the clique information in the original graph and the pairwise clique correspondence constrained by the one-to-one matching. Since the objective function is not jointly convex to both latent variables, we decompose it into two consecutive steps for optimization: 1) clique-to-clique similarity measure by preserving local unary and pairwise correspondences; 2) graph-to-graph similarity measure by preserving global clique-to-clique correspondence. Extensive experiments on the synthetic data and real images show that the proposed method can outperform representative methods especially when both noise and outliers exist.

@InProceedings{Nie_2015_CVPR,author = {Nie, Wei-Zhi and Liu, An-An and Gao, Zan and Su, Yu-Ting},title = {Clique-Graph Matching by Preserving Global & Local Structure},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling

Abstract: Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas},title = {Appearance-Based Gaze Estimation in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jiyoung Jung, Joon-Young Lee, In So Kweon

Abstract: We present an outdoor photometric stereo method using images captured in a single day. We simulate a sky hemisphere for each image according to its GPS and timestamp, and parameterize the obtained sky hemisphere into a quadratic skylight and a Gaussian sunlight distribution. Unlike previous works which usually model outdoor illumination as a sum of constant ambient light and a distant point light, our method models natural illumination according to a popular sky model and thus provides sufficient constraints for shape reconstruction from one day images. We generate pixel profiles of uniformly sampled unit vectors for the corresponding time of captures and evaluate them using correlation with the actual pixel profiles. The estimated surface normal is refined by MRF optimization. We have tested our method to recover objects and scenes of various sizes in real-world outdoor daylight.

@InProceedings{Jung_2015_CVPR,author = {Jung, Jiyoung and Lee, Joon-Young and So Kweon, In},title = {One-Day Outdoor Photometric Stereo via Skylight Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhizhong Li, Deli Zhao, Zhouchen Lin, Edward Y. Chang

Abstract: The Riemannian three-factor matrix completion (R3MC) algorithm is one of the state-of-the-art geometric optimization methods for the low-rank matrix completion problem. It is a nonlinear conjugate-gradient method optimizing on a quotient Riemannian manifold. In the line search step, R3MC approximates the minimum point on the searching curve by minimizing on the line tangent to the curve. However, finding the exact minimum point by iteration is too expensive. We address this issue by proposing a new retrac with a minimizing property. This special property provides the exact minimization for the line search by establishing correspondences between points on the searching curve and points on the tangent line. Accelerated R3MC, which is R3MC equipped with this new retraction, outperforms the original algorithm and other geometric algorithms for matrix completion in our empirical study.

@InProceedings{Li_2015_CVPR,author = {Li, Zhizhong and Zhao, Deli and Lin, Zhouchen and Chang, Edward Y.},title = {A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Bing Su, Xiaoqing Ding, Changsong Liu, Ying Wu

Abstract: Many discriminant analysis methods such as LDA and HLDA actually maximize the average pairwise distances between classes, which often causes the class separation problem. Max-min distance analysis (MMDA) addresses this problem by maximizing the minimum pairwise distance in the latent subspace, but it is developed under the homoscedastic assumption. This paper proposes Heteroscedastic MMDA (HMMDA) methods that explore the discriminative information in the difference of intra-class scatters for dimensionality reduction. WHMMDA maximizes the minimal pairwise Chenoff distance in the whitened space. OHMMDA incorporates this objective and the minimization of class compactness into a trace quotient formulation and imposes an orthogonal constraint to the final transformation, which can be solved by a bisection search algorithm. Two variants of OHMMDA are further proposed to encode the margin information. Experiments on several UCI Machine Learning datasets and the Yale Face database demonstrate the effectiveness of the proposed HMMDA methods.

@InProceedings{Su_2015_CVPR,author = {Su, Bing and Ding, Xiaoqing and Liu, Changsong and Wu, Ying},title = {Heteroscedastic Max-Min Distance Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Sparse Composite Quantization

Ting Zhang, Guo-Jun Qi, Jinhui Tang, Jingdong Wang

Abstract: The quantization techniques have shown competitive performance in approximate nearest neighbor search. The state-of-the-art algorithm, composite quantization, takes advantage of the compositionabity, i.e., the vector approximation accuracy, as opposed to product quantization and Cartesian k-means. However, we have observed that the runtime cost of computing the distance table in composite quantization, which is used as a lookup table for fast distance computation, becomes nonnegligible in real applications, e.g., reordering the candidates retrieved from the inverted index when handling very large scale databases. To address this problem, we develop a novel approach, called sparse composite quantization, which constructs sparse dictionaries. The benefit is that the distance evaluation between the query and the dictionary element (a sparse vector) is accelerated using the efficient sparse vector operation, and thus the cost of distance table computation is reduced a lot. Experiment results on large scale ANN retrieval tasks (1M SIFTs and 1B SIFTs) and applications to object retrieval show that the proposed approach yields competitive performance: superior search accuracy to product quantization and Cartesian k-means with almost the same computing cost, and much faster ANN search than composite quantization with the same level of accuracy.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Ting and Qi, Guo-Jun and Tang, Jinhui and Wang, Jingdong},title = {Sparse Composite Quantization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh

Abstract: Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.

@InProceedings{Vedantam_2015_CVPR,author = {Vedantam, Ramakrishna and Lawrence Zitnick, C. and Parikh, Devi},title = {CIDEr: Consensus-Based Image Description Evaluation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Baochang Zhang, Alessandro Perina, Vittorio Murino, Alessio Del Bue

Abstract: The fact that image data samples lie on a manifold has been successfully exploited in many learning and inference problems. In this paper we leverage the specific structure of data in order to improve recognition accuracies in general recognition tasks. In particular we propose a novel framework that allows to embed manifold priors into sparse representation-based classification (SRC) approaches. We also show that manifold constraints can be transferred from the data to the optimized variables if these are linearly correlated. Using this new insight, we define an efficient alternating direction method of multipliers (ADMM) that can consistently integrate the manifold constraints during the optimization process. This is based on the property that we can recast the problem as the projection over the manifold via a linear embedding method based on the Geodesic distance. The proposed approach is successfully applied on face, digit, action and objects recognition showing a consistently increase on performance when compared to the state of the art.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Baochang and Perina, Alessandro and Murino, Vittorio and Del Bue, Alessio},title = {Sparse Representation Classification With Manifold Constraints Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tianmin Shu, Dan Xie, Brandon Rothrock, Sinisa Todorovic, Song Chun Zhu

Abstract: With the advent of drones, aerial video analysis becomes increasingly important; yet, it has received scant attention in the literature. This paper addresses a new problem of parsing low-resolution aerial videos of large spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning roles to people engaged in events.  We propose a novel framework aimed at conducting joint inference of the above tasks, as reasoning about each in isolation typically fails in our setting. Given noisy tracklets of people and detections of large objects and scene surfaces (e.g., building, grass), we use a spatiotemporal AND-OR graph to drive our joint inference, using Markov Chain Monte Carlo and dynamic programming. We also introduce a new formalism of spatiotemporal templates characterizing latent sub-events. For evaluation, we have collected and released a new aerial videos dataset using a hex-rotor flying over picnic areas rich with group events. Our results demonstrate that we successfully address above inference tasks under challenging conditions.

@InProceedings{Shu_2015_CVPR,author = {Shu, Tianmin and Xie, Dan and Rothrock, Brandon and Todorovic, Sinisa and Chun Zhu, Song},title = {Joint Inference of Groups, Events and Human Roles in Aerial Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wuyuan Xie, Chengkai Dai, Charlie C. L. Wang

Abstract: We tackle the problem of photometric stereo under near point lighting in this paper. Different from the conventional formulation of photometric stereo that assumes parallel lighting, photometric stereo under the near point lighting condition is a nonlinear problem as the local surface normals are coupled with its distance to the camera as well as the light sources. To solve this non-linear problem of PS with near point lighting, a local/global mesh deformation approach is developed in our work to determine the position and the orientation of a facet simultaneously, where each facet is corresponding to a pixel in the image captured by the camera. Unlike nonlinear optimization schemes, the mesh deformation in our approach is decoupled into an iteration of interlaced steps of local projection and global blending. Experimental results verify that our method can generate accurate estimation of surface shape under near point lighting in a few iterations. Besides, this approach is robust to errors on the positions of light sources and is easy to be implemented.

@InProceedings{Xie_2015_CVPR,author = {Xie, Wuyuan and Dai, Chengkai and Wang, Charlie C. L.},title = {Photometric Stereo With Near Point Lighting: A Solution by Mesh Deformation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Maggie Wigness, Bruce A. Draper, J. Ross Beveridge

Abstract: Visual classifiers are part of many applications including surveillance, autonomous navigation and scene understanding. The raw data used to train these classifiers is abundant and easy to collect but lacks labels. Labels are necessary for training supervised classifiers, but the labeling process requires significant human effort. Techniques like active learning and group-based labeling have emerged to help reduce the labeling workload. However, the possibility of collecting label noise affects either the efficiency of these systems or the performance of the trained classifiers. Further, many introduce latency by iteratively re-training classifiers or re-clustering data. We introduce a technique that searches for structural change in hierarchically clustered data to identify a set of clusters that span a spectrum of visual concept granularities. This allows us to efficiently label clusters with less label noise and produce high performing classifiers. The data is hierarchically clustered only once, eliminating latency during the labeling process. Using benchmark data we show that collecting labels with our approach is more efficient than existing labeling techniques, and achieves higher classification accuracy. Finally, we demonstrate the speed and efficiency of our system using real-world data collected for an autonomous navigation task.

@InProceedings{Wigness_2015_CVPR,author = {Wigness, Maggie and Draper, Bruce A. and Ross Beveridge, J.},title = {Efficient Label Collection for Unlabeled Image Datasets},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Shijie Xiao, Wen Li, Dong Xu, Dacheng Tao

Abstract: Low rank representation (LRR) has shown promising performance for various computer vision applications such as face clustering. Existing algorithms for solving LRR usually depend on its two-variable formulation which contains the original data matrix. In this paper, we develop a fast LRR solver called FaLRR, by reformulating LRR as a new optimization problem with regard to factorized data (which is obtained by skinny SVD of the original data matrix). The new formulation benefits the corresponding optimization and theoretical analysis. Specifically, to solve the resultant optimization problem, we propose a new algorithm which is not only efficient but also theoretically guaranteed to obtain a globally optimal solution. Regarding the theoretical analysis, the new formulation is helpful for deriving some interesting properties of LRR. Last but not least, the proposed algorithm can be readily incorporated into an existing distributed framework of LRR for further acceleration. Extensive experiments on synthetic and real-world datasets demonstrate that our FaLRR achieves order-of-magnitude speedup over existing LRR solvers, and the efficiency can be further improved by incorporating our algorithm into the distributed framework of LRR.

@InProceedings{Xiao_2015_CVPR,author = {Xiao, Shijie and Li, Wen and Xu, Dong and Tao, Dacheng},title = {FaLRR: A Fast Low Rank Representation Solver},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Salman H. Khan, Xuming He, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri

Abstract: Objects' spatial layout estimation and clutter identification are two important tasks to understand indoor scenes. We propose to solve both of these problems in a joint framework using RGBD images of indoor scenes. In contrast to recent approaches which focus on either one of these two problems, we perform `fine grained structure categorization' by predicting all the major objects and simultaneously labeling the cluttered regions. A conditional random field model is proposed to incorporate a rich set of local appearance, geometric features and interactions between the scene elements. We take a structural learning approach with a loss of 3D localisation to estimate the model parameters from a large annotated RGBD dataset, and a mixed integer linear programming formulation for inference. We demonstrate that our approach is able to detect cuboids and estimate cluttered regions across many different object and scene categories in the presence of occlusion, illumination and appearance variations.

@InProceedings{Khan_2015_CVPR,author = {Khan, Salman H. and He, Xuming and Bennamoun, Mohammed and Sohel, Ferdous and Togneri, Roberto},title = {Separating Objects and Clutter in Indoor Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We present a method for simulating makeup in a face image. To generate realistic results without detailed geometric and reflectance measurements of the user, we propose to separate the image into intrinsic image layers and alter them according to proposed adaptations of physically-based reflectance models. Through this layer manipulation, the measured properties of cosmetic products are applied while preserving the appearance characteristics and lighting conditions of the target face. This approach is demonstrated on various forms of cosmetics including foundation, blush, lipstick, and eye shadow. Experimental results exhibit a close approximation to ground truth images, without artifacts such as transferred personal features and lighting effects that degrade the results of image-based makeup transfer methods.

@InProceedings{Li_2015_CVPR,author = {Li, Chen and Zhou, Kun and Lin, Stephen},title = {Simulating Makeup Through Physics-Based Manipulation of Intrinsic Image Layers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hamed Kiani Galoogahi, Terence Sim, Simon Lucey

Abstract: Correlation filters take advantage of specific properties in the Fourier domain allowing them to be estimated efficiently: O(ND log D) in the frequency domain, versus O(D^3 + ND^2) spatially where D is signal length, and N is the number of signals.  Recent extensions to correlation filters, such as MOSSE, have reignited interest of their use in the vision community due to their robustness and attractive computational properties. In this paper we demonstrate, however, that this computational efficiency comes at a cost. Specifically, we demonstrate that only 1/D proportion of shifted examples are unaffected by boundary effects which has a dramatic effect on detection/tracking performance. In this paper, we propose a novel approach to correlation filter estimation that: (i) takes advantage of inherent computational redundancies in the frequency domain, (ii) dramatically reduces boundary effects, and (iii) is able to implicitly exploit all possible patches densely extracted from training examples during learning process. Impressive object tracking and detection results are presented in terms of both accuracy and computational efficiency.

@InProceedings{Galoogahi_2015_CVPR,author = {Kiani Galoogahi, Hamed and Sim, Terence and Lucey, Simon},title = {Correlation Filters With Limited Boundaries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Syed Zulqarnain Gilani, Faisal Shafait, Ajmal Mian

Abstract: We present an algorithm for automatic detection of a large number of anthropometric landmarks on 3D faces. Our approach does not use texture and is completely shape based in order to detect landmarks that are morphologically significant. The proposed algorithm evolves level set curves with adaptive geometric speed functions to automatically extract effective seed points for dense correspondence. Correspondences are established by minimizing the bending energy between patches around seed points of given faces to those of a reference face. Given its hierarchical structure, our algorithm is capable of establishing thousands of correspondences between a large number of faces. Finally, a morphable model based on the dense corresponding points is fitted to an unseen query face for transfer of correspondences and hence automatic detection of landmarks. The proposed algorithm can detect any number of pre-defined landmarks including subtle landmarks that are even difficult to detect manually. Extensive experimental comparison on two benchmark databases containing 6,507 scans shows that our algorithm outperforms six state of the art algorithms.

@InProceedings{Gilani_2015_CVPR,author = {Zulqarnain Gilani, Syed and Shafait, Faisal and Mian, Ajmal},title = {Shape-Based Automatic Detection of a Large Number of 3D Facial Landmarks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Philip Saponaro, Scott Sorensen, Abhishek Kolagunda, Chandra Kambhamettu

Abstract: Material classification is an important area of research in computer vision. Typical algorithms use color and texture information for classification, but there are problems due to varying lighting conditions and diversity of colors in a single material class. In this work we study the use of long wave infrared (i.e. thermal) imagery for material classification. Thermal imagery has the benefit of relative invariance to color changes,  invariance to lighting conditions, and can even work in the dark. We collect a database of 21 different material classes with both color and thermal imagery. We develop a set of features that describe water permeation and heating/cooling properties, and test several variations on these methods to obtain our final classifier. The results show that the proposed method outperforms typical color and texture features, and when combined with color information, the results are improved further.

@InProceedings{Saponaro_2015_CVPR,author = {Saponaro, Philip and Sorensen, Scott and Kolagunda, Abhishek and Kambhamettu, Chandra},title = {Material Classification With Thermal Imagery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jing Shao, Kai Kang, Chen Change Loy, Xiaogang Wang

Abstract: Crowded scene understanding is a fundamental problem in computer vision. In this study, we develop a multi-task deep model to jointly learn and combine appearance and motion features for crowd understanding. We propose crowd motion channels as the input of the deep model and the channel design is inspired by generic properties of crowd systems. To well demonstrate our deep model, we construct a new large-scale WWW Crowd dataset with 10000 videos from 8257 crowded scenes, and build an attribute set with 94 attributes on WWW. We further measure user study performance on WWW and compare this with the proposed deep models. Extensive experiments show that our deep models display significant performance improvements in cross-scene attribute recognition compared to strong crowd-related feature-based baselines, and the deeply learned features behave an superior performance in multi-task learning.

@InProceedings{Shao_2015_CVPR,author = {Shao, Jing and Kang, Kai and Change Loy, Chen and Wang, Xiaogang},title = {Deeply Learned Attributes for Crowded Scene Understanding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We revisit the well-known problem of gaze correction and present a solution based on supervised machine learning. At training time, our system observes pairs of images, where each pair contains the face of the same person with a fixed angular difference in gaze direction. It then learns to synthesize the second image of a pair from the first one. After learning, the system becomes able to redirect the gaze of a previously unseen person by the same angular difference. Unlike many previous solutions to gaze problem in videoconferencing, ours is purely monocular, i.e. it does not require any hardware apart from an in-built web-camera of a laptop.  We base our machine learning implementation on a special kind of decision forests that predict a displacement (flow) vector for each pixel in the input image. As a result, our system is highly efficient (runs in real-time on a single core of a modern laptop). In the paper, we demonstrate results on a variety of videoconferencing frames and evaluate the method quantitatively on the hold-out set of registered images. The supplementary video shows example sessions of our system at work.

@InProceedings{Kononenko_2015_CVPR,author = {Kononenko, Daniil and Lempitsky, Victor},title = {Learning To Look Up: Realtime Monocular Gaze Correction Using Machine Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts.

@InProceedings{Xin_2015_CVPR,author = {Xin, Bo and Tian, Yuan and Wang, Yizhou and Gao, Wen},title = {Background Subtraction via Generalized Fused Lasso Foreground Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Do object part localization methods produce bilaterally symmetric results on mirror images? Surprisingly not, even though state of the art methods augment the training set with mirrored images. In this paper we take a closer look into this issue. We first introduce the concept of mirrorability as the ability of a model to produce symmetric results in mirrored images and introduce a corresponding measure, namely the \textit{mirror error} that is defined as the difference between the detection result on an image  and the mirror of the detection result on its mirror image. We evaluate the mirrorability of several state of the art algorithms in two of the most intensively studied problems, namely human pose estimation and face alignment. Our experiments lead to several interesting findings: 1) Most of state of the art methods struggle to preserve the mirror symmetry, despite the fact that they do have very similar overall performance on the original and mirror images; 2) the low mirrorability is not caused by training or testing sample bias - all algorithms are trained on both the original images and their mirrored versions; 3) the mirror error is strongly correlated to the localization/alignment error (with correlation coefficients around 0.7). Since the mirror error is calculated without knowledge of the ground truth, we show two interesting applications - in the first it is used to guide the selection of difficult samples and in the second to give feedback in a popular Cascaded Pose Regression method for face alignment.

@InProceedings{Yang_2015_CVPR,author = {Yang, Heng and Patras, Ioannis},title = {Mirror, Mirror on the Wall, Tell Me, Is the Error Small?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yukun Zhu, Raquel Urtasun, Ruslan Salakhutdinov, Sanja Fidler

Abstract: In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection. We frame the problem as inference in a Markov Random Field, in which each detection hypothesis scores object appearance as well as contextual information using Convolutional Neural Networks, and allows the hypothesis to choose and score a segment out of a large pool of accurate object segmentation proposals. This enables the detector to incorporate additional evidence when it is available and thus results in more accurate detections. Our experiments show an improvement of 4.1% in mAP over the R-CNN baseline on PASCAL VOC 2010, and 1.4% over the current state-of-the-art, demonstrating the power of our approach.

@InProceedings{Zhu_2015_CVPR,author = {Zhu, Yukun and Urtasun, Raquel and Salakhutdinov, Ruslan and Fidler, Sanja},title = {segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici

Abstract: Convolutional neural networks (CNNs) have been exten- sively applied for image recognition problems giving state- of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image infor- mation across a video over longer time periods than previ- ously attempted. We propose two methods capable of han- dling full length videos. The first method explores various convolutional temporal feature pooling architectures, ex- amining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improve- ments over previously published results on the Sports 1 mil- lion dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.2% vs. 87.9%) and without additional optical flow information (82.6% vs. 72.8%).

@InProceedings{Ng_2015_CVPR,author = {Yue-Hei Ng, Joe and Hausknecht, Matthew and Vijayanarasimhan, Sudheendra and Vinyals, Oriol and Monga, Rajat and Toderici, George},title = {Beyond Short Snippets: Deep Networks for Video Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Situational Object Boundary Detection

Abstract: Intuitively, the appearance of true object boundaries varies from image to image. Hence the usual monolithic approach of training a single boundary predictor and applying it to all images regardless of their content  is bound to be suboptimal.  In this paper we therefore propose Situational Object Boundary Detection: we first define a variety of situations and train a specialized object boundary detector for each of them using [Dollar13]. Then given a test image, we classify it into these situations using its context, which we model by global image appearance. We apply the corresponding situational object boundary detectors, and fuse them based on the classification probabilities.  In experiments on  ImageNet [Russakovsky14], Microsoft COCO [Lin], and Pascal VOC 2012 segmentation [Everingham14] we show that our situational object boundary detection gives significant improvements over a monolithic approach. Additionally, our method substantially outperforms [Hariharan11iccv] on semantic contour detection on their SBD dataset.

@InProceedings{Uijlings_2015_CVPR,author = {Uijlings, Jasper R. R. and Ferrari, Vittorio},title = {Situational Object Boundary Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chavdar Papazov, Tim K. Marks, Michael Jones

Abstract: We present a real-time system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. We introduce a novel triangular surface patch (TSP) descriptor, which encodes the shape of the 3D surface of the face within a triangular area. The proposed descriptor is viewpoint invariant, and it is robust to noise and to variations in the data resolution. Using a fast nearest neighbor lookup, TSP descriptors from an input depth map are matched to the most similar ones that were computed from synthetic head models in a training phase. The matched triangular surface patches in the training set are used to compute estimates of the 3D head pose and facial landmark positions in the input depth map. By sampling many TSP descriptors, many votes for pose and landmark positions are generated which together yield robust final estimates.  We evaluate our approach on the publicly available Biwi Kinect Head Pose Database to compare it against state-of-the-art methods. Our results show a significant improvement in the accuracy of both pose and landmark location estimates while maintaining real-time speed.

@InProceedings{Papazov_2015_CVPR,author = {Papazov, Chavdar and Marks, Tim K. and Jones, Michael},title = {Real-Time 3D Head Pose and Facial Landmark Estimation From Depth Images Using Triangular Surface Patch Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Saurabh Gupta, Pablo Arbelaez, Ross Girshick, Jitendra Malik

Abstract: The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art, while being an order of magnitude faster.

@InProceedings{Gupta_2015_CVPR,author = {Gupta, Saurabh and Arbelaez, Pablo and Girshick, Ross and Malik, Jitendra},title = {Aligning 3D Models to RGB-D Images of Cluttered Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Lingqiao Liu, Chunhua Shen, Anton van den Hengel

Abstract: A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image descriptor, and that doing so leads to impressive performance at a range of image classification tasks. Most of these studies, if not all, adopt activations of the fully-connected layer of a DCNN as the image or region representation and it is believed that convolutional layer activations are less discriminative.  This paper, however, advocates that if used appropriately, convolutional layer activations constitute a powerful image representation. This is achieved by adopting a new technique proposed in this paper called cross-convolutional-layer pooling. More specifically, it extracts subarrays of feature maps of one convolutional layer as local features, and pools the extracted features with the guidance of the feature maps of the successive convolutional layer. Compared with existing methods that apply DCNNs in the similar local feature setting, the proposed method avoids the input image style mismatching issue which is usually encountered when applying fully connected layer activations to describe local regions. Also, the proposed method is easier to implement since it is codebook free and does not have any tuning parameters. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve comparable or in some cases significantly better performance than existing fully-connected layer based image representations.

@InProceedings{Liu_2015_CVPR,author = {Liu, Lingqiao and Shen, Chunhua and van den Hengel, Anton},title = {The Treasure Beneath Convolutional Layers: Cross-Convolutional-Layer Pooling for Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jan Reininghaus, Stefan Huber, Ulrich Bauer, Roland Kwitt

Abstract: Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we establish such a connection by designing a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data. We show that this kernel is positive definite and prove its stability with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets for 3D shape classification/retrieval and texture recognition show considerable performance gains of the proposed method compared to an alternative approach that is based on the recently introduced persistence landscapes.

@InProceedings{Reininghaus_2015_CVPR,author = {Reininghaus, Jan and Huber, Stefan and Bauer, Ulrich and Kwitt, Roland},title = {A Stable Multi-Scale Kernel for Topological Machine Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yan Li, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen

Abstract: Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video clip are usually represented quite differently. Typically, face image is represented as point (i.e., vector) in Euclidean space, while video clip is seemingly modeled as a point (e.g., covariance matrix) on some particular Riemannian manifold in the light of its recent promising success. It thus incurs a new hashing-based retrieval problem of matching two heterogeneous representations, respectively in Euclidean space and Riemannian manifold. This work makes the first attempt to embed the two heterogeneous spaces into a common discriminant Hamming space. Specifically, we propose Hashing across Euclidean space and Riemannian manifold (HER) by deriving a unified framework to firstly embed the two spaces into corresponding reproducing kernel Hilbert spaces, and then iteratively optimize the intra- and inter-space Hamming distances in a maxmargin framework to learn the hash functions for the two spaces. Extensive experiments demonstrate the impressive superiority of our method over the state-of-the-art competitive hash learning methods.

@InProceedings{Li_2015_CVPR,author = {Li, Yan and Wang, Ruiping and Huang, Zhiwu and Shan, Shiguang and Chen, Xilin},title = {Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian Manifold},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yair Poleg, Tavi Halperin, Chetan Arora, Shmuel Peleg

Abstract: While egocentric cameras like GoPro are gaining popularity, the videos they capture are long, boring, and difficult to watch from start to end. Fast forwarding (i.e. frame sampling) is a natural choice for faster video browsing. However, this accentuates the shake caused by natural head motion, making the fast forwarded video useless.  We propose EgoSampling, an adaptive frame sampling that gives more stable fast forwarded videos. Adaptive frame sampling is formulated as energy minimization, whose optimal solution can be found in polynomial time.  In addition, egocentric video taken while walking suffers from the left-right movement of the head as the body weight shifts from one leg to another. We turn this drawback into a feature: Stereo video can be created by sampling the frames from the left most and right most head positions of each step, forming approximate stereo-pairs.

@InProceedings{Poleg_2015_CVPR,author = {Poleg, Yair and Halperin, Tavi and Arora, Chetan and Peleg, Shmuel},title = {EgoSampling: Fast-Forward and Stereo for Egocentric Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui

Abstract: The "interpretation through synthesis", i.e. Active Appearance Models (AAMs) method, has received considerable attention over the past decades. It aims at "explaining" face images by synthesizing them via a parameterized model of appearance. It is quite challenging due to appearance variations of human face images, e.g. facial poses, occlusions, lighting, low resolution, etc. Since these variations are mostly non-linear, it is impossible to represent them in a linear model, such as Principal Component Analysis (PCA). This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations. In this approach, three crucial components represented in hierarchical layers are modeled using the Deep Boltzmann Machines (DBM) to robustly capture the variations of facial shapes and appearances. DAMs are therefore superior to AAMs in inferring a representation for new face images under various challenging conditions. In addition, DAMs have ability to generate a compact set of parameters in higher level representation that can be used for classification, e.g. face recognition and facial age estimation. The proposed approach is evaluated in facial image reconstruction, facial super-resolution on two databases, i.e. LFPW and Helen. It is also evaluated on FG-NET database for the problem of age estimation.

@InProceedings{Duong_2015_CVPR,author = {Nhan Duong, Chi and Luu, Khoa and Gia Quach, Kha and Bui, Tien D.},title = {Beyond Principal Components: Deep Boltzmann Machines for Face Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Social Saliency Prediction

Abstract: This paper presents a method to predict social saliency, the likelihood of joint attention, given an input image or video by leveraging the social interaction data captured by first person cameras. Inspired by electric dipole moments, we introduce a social formation feature that encodes the geometric relationship between joint attention and its social formation. We learn this feature from the first person social interaction data where we can precisely measure the locations of joint attention and its associated members in 3D. An ensemble classifier is trained to learn the geometric relationship. Using the trained classifier, we predict social saliency in real-world scenes with multiple social groups including scenes from team sports captured in a third person view. Our representation does not require directional measurements such as gaze directions. A geometric analysis of social interactions in terms of the F-formation theory is also presented.

@InProceedings{Park_2015_CVPR,author = {Soo Park, Hyun and Shi, Jianbo},title = {Social Saliency Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Won Hwa Kim, Barbara B. Bendlin, Moo K. Chung, Sterling C. Johnson, Vikas Singh

Abstract: Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience. However, when there are systematic variations in the images due to parameters changes such as changes in the scanner protocol, hardware changes, or when combining data from multi-site studies, the statistical analysis becomes problematic. Motivated by this scenario, the goal of this paper is to develop a unified statistical solution to the problem of systematic variations in statistical image analysis. Based in part on recent literature in harmonic analysis on diffusion maps, we propose an algorithm which compares operators that are resilient to the systematic variations described above. These operators are derived from the empirical measurements of the image data and provide an efficient surrogate to capturing the actual changes across images. We also establish a connection between our method to the design of Wavelets in non-Euclidean space. To evaluate the proposed ideas, we present various experimental results on detecting changes in simulations as well as show how the method offers improved statistical power in the analysis of longitudinal real PIB-PET imaging data acquired from participants at risk for Alzheimer's disease(AD).

@InProceedings{Kim_2015_CVPR,author = {Hwa Kim, Won and Bendlin, Barbara B. and Chung, Moo K. and Johnson, Sterling C. and Singh, Vikas},title = {Statistical Inference Models for Image Datasets With Systematic Variations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, Lubomir Bourdev

Abstract: We explore the task of recognizing peoples' identities in  photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset, consisting of over 60000 instances of ~2000 individuals collected from public Flickr photo albums. With only about half of the person images containing a frontal face, the recognition task is very challenging due to the large variations in pose, clothing, camera viewpoint, image resolution and illumination.   We propose the Pose Invariant PErson Recognition (PIPER) method, which accumulates the cues of poselet-level person recognizers trained by deep convolutional networks to discount for the pose variations, combined with a face recognizer and a global recognizer. Experiments on three different settings confirm that in our unconstrained setup PIPER significantly improves on the performance of DeepFace, which is one of the best face recognizers as measured on the LFW dataset.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Ning and Paluri, Manohar and Taigman, Yaniv and Fergus, Rob and Bourdev, Lubomir},title = {Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Daniela Giordano, Francesca Murabito, Simone Palazzo, Concetto Spampinato

Abstract: In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets. The method does not make any specific assumptions about the videos and relies on how objects are perceived by humans according to Gestalt laws. Initially, we rapidly generate a coarse foreground segmentation, which provides predictions about motion regions by analyzing how superpixel segmentation changes in consecutive frames. We then exploit these location priors to refine the initial segmentation by optimizing an energy function based on appearance and perceptual organization, only on regions where motion is observed.  We evaluated our method on complex and challenging video sequences and it showed significant performance improvements over recent state-of-the-art methods, being also fast enough to be used for "on-the-fly" processing.

@InProceedings{Giordano_2015_CVPR,author = {Giordano, Daniela and Murabito, Francesca and Palazzo, Simone and Spampinato, Concetto},title = {Superpixel-Based Video Object Segmentation Using Perceptual Organization and Location Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. The aim is to transfer fine structures of guidance signals to input images, restoring noisy or altered structures. One of main drawbacks in such a data-dependent framework is that it does not handle differences in structure between guidance and input images. We address this problem by jointly leveraging structural information of guidance and input images. Image filtering is formulated as a nonconvex optimization problem, which is solved by the majorization-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local mini- mum. It effectively controls image structures at different scales and can handle a variety of types of data from different sensors. We demonstrate the flexibility and effectiveness of our model in several applications including depth super- resolution, scale-space filtering, texture removal, flash/non- flash denoising, and RGB/NIR denoising.

@InProceedings{Ham_2015_CVPR,author = {Ham, Bumsub and Cho, Minsu and Ponce, Jean},title = {Robust Image Filtering Using Joint Static and Dynamic Guidance},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We present a new deep network layer called ``Dynamic Convolutional Layer" which is a generalization of the convolutional layer. The conventional convolutional layer uses filters that are learned during training and are held constant during testing. In contrast, the dynamic convolutional layer uses filters that will vary from input to input during testing. This is achieved by learning a function that maps the input to the filters. We apply the dynamic convolutional layer to the application of short range weather prediction and show performance improvements compared to other baselines.

@InProceedings{Klein_2015_CVPR,author = {Klein, Benjamin and Wolf, Lior and Afek, Yehuda},title = {A Dynamic Convolutional Layer for Short Range Weather Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Jigsaw-puzzle solving is necessary in many applications, including biology, archaeology, and every-day life. In this paper we consider the square jigsaw puzzle problem, where the goal is to reconstruct the image from a set of non-overlapping, unordered, square puzzle parts. Our key contribution is a fast, fully-automatic, and general solver, which assumes no prior knowledge about the original image. It is general in the sense that it can handle puzzles of unknown size, with pieces of unknown orientation, and even puzzles with missing pieces. Moreover, it can handle all the above, given pieces from multiple puzzles. Through an extensive evaluation we show that our approach outperforms state-of-the-art methods on commonly-used datasets.

@InProceedings{Paikin_2015_CVPR,author = {Paikin, Genady and Tal, Ayellet},title = {Solving Multiple Square Jigsaw Puzzles With Missing Pieces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Maryam Jaberi, Marianna Pensky, Hassan Foroosh

Abstract: We study the simultaneous detection of multiple structures in the presence of overwhelming number of outliers in a large population of points. Our approach reduces the problem to sampling an extremely sparse subset of the original population of data in one grab, followed by an unsupervised clustering of the population based on a set of instantiated models from this sparse subset. We show that the problem can be modeled using a multivariate hypergeometric distribution, and derive accurate mathematical bounds to determine a tight approximation to the sample size, leading thus to a sparse sampling strategy. We evaluate the method thoroughly in terms of accuracy, its behavior against varying input parameters, and comparison against existing methods, including the state of the art. The key features of the proposed approach are: (i) sparseness of the sampled set, where the level of sparseness is independent of the population size and the distribution of data, (ii) robustness in the presence of overwhelming number of outliers, and (iii) unsupervised detection of all model instances, i.e. without requiring any prior knowledge of the number of embedded structures. To demonstrate the generic nature of the proposed method, we show experimental results on different computer vision problems, such as detection of physical structures e.g. lines, planes, etc., as well as more abstract structures such as fundamental matrices, and homographies in multi-body structure from motion.

@InProceedings{Jaberi_2015_CVPR,author = {Jaberi, Maryam and Pensky, Marianna and Foroosh, Hassan},title = {SWIFT: Sparse Withdrawal of Inliers in a First Trial},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


VIP: Finding Important People in Images

Clint Solomon Mathialagan, Andrew C. Gallagher, Dhruv Batra

Abstract: People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context of the event. This paper analyzes the concept of the importance of individuals in group photographs. We address two specific questions - Given an image, who are the most important individuals in it? Given multiple images of a person, which image depicts the person in the most important role? We introduce a measure of importance of people in images and investigate the correlation between importance and visual saliency. We find that not only can we automatically predict the importance of people from purely visual cues, incorporating this predicted importance results in significant improvement in applications such as im2text (generating sentences that describe images of groups of people).

@InProceedings{Mathialagan_2015_CVPR,author = {Solomon Mathialagan, Clint and Gallagher, Andrew C. and Batra, Dhruv},title = {VIP: Finding Important People in Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Konstantinos Rematas, Basura Fernando, Frank Dellaert, Tinne Tuytelaars

Abstract: As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content. In this paper, we propose dataset fingerprints, a new and powerful method based on data mining that extracts meaningful patterns from a set of images. The discovered patterns are compositions of discriminative mid-level features that co-occur in several images. Compared to earlier work, ours stands out because i) it's fully unsupervised, ii) discovered patterns cover large parts of the images,often corresponding to full objects or meaningful parts thereof, and iii) different patterns are connected based on co-occurrence, allowing a user to ``browse'' / ``surf'' the images from one pattern to the next and to group patterns in a semantically meaningful manner.

@InProceedings{Rematas_2015_CVPR,author = {Rematas, Konstantinos and Fernando, Basura and Dellaert, Frank and Tuytelaars, Tinne},title = {Dataset Fingerprints: Exploring Image Collections Through Data Mining},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Extracting high-resolution information from highly degraded facial images is an important problem with several applications in science and technology. Here we describe a single frame super resolution technique that uses a transport-based formulation of the problem. The method consists of a training and a testing phase. In the training phase, a nonlinear Lagrangian model of high-resolution facial appearance is constructed fully automatically. In the testing phase,  the resolution of a degraded image is enhanced by finding the model parameters that best fit the given low resolution data. We test the approach on two face datasets, namely the extended Yale Face Database B and the AR face datasets, and compare it to state of the art methods. The proposed method  outperforms existing solutions in problems related to enhancing images of very low resolution.

@InProceedings{Kolouri_2015_CVPR,author = {Kolouri, Soheil and Rohde, Gustavo K.},title = {Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mao Ye, Yu Zhang, Ruigang Yang, Dinesh Manocha

Abstract: We present a practical and inexpensive method to reconstruct 3D scenes that include piece-wise planar transparent objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, in which glass structures are common. These large structures are often invisible to cameras or even our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-size reconstruction setting. Our approach augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure distance to any objects, including transparent surfaces. We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. Our current hardware setup can generate only one additional point measurement per frame, yet our fusion algorithm is able to generate satisfactory reconstruction results based on our probabilistic model. We highlight the performance in many challenging indoor benchmarks.

@InProceedings{Ye_2015_CVPR,author = {Ye, Mao and Zhang, Yu and Yang, Ruigang and Manocha, Dinesh},title = {3D Reconstruction in the Presence of Glasses by Acoustic and Stereo Fusion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yeqing Li, Chen Chen, Fei Yang, Junzhou Huang

Abstract: The definition of the similarity measure is an essential component in image registration. In this paper, we propose a novel similarity measure for registration of two or more images. The proposed method is motivated by that the optimally registered images can be deeply sparsified in the gradient domain and frequency domain, with the separation of a sparse tensor of errors. One of the key advantages of the proposed similarity measure is its robustness to severe intensity distortions, which widely exist on medical images, remotely sensed images and natural photos due to the difference of acquisition modalities or illumination conditions. Two efficient algorithms are proposed to solve the batch image registration and pair registration problems in a unified framework. We validate our method on extensive challenging datasets. The experimental results demonstrate the robustness, accuracy and efficiency of our method over 9 traditional and state-of-the-art algorithms on synthetic images and a wide range of real-world applications.

@InProceedings{Li_2015_CVPR,author = {Li, Yeqing and Chen, Chen and Yang, Fei and Huang, Junzhou},title = {Deep Sparse Representation for Robust Image Registration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Robust object tracking is a challenging task in computer vision. To better solve the partial occlusion issue, part-based methods are widely used in visual object trackers. However, due to the complicated online training and updating process, most of these part-based trackers cannot run in real-time. Correlation filters have been used in tracking tasks recently because of the high efficiency. However, the conventional correlation filter based trackers cannot deal with occlusion. Furthermore, most correlation filter based trackers fix the scale and rotation of the target which makes the trackers unreliable in long-term tracking tasks. In this paper, we propose a novel tracking method which track objects based on parts with multiple correlation filters. Our method can run in real-time. Additionally, the Bayesian inference framework and a structural constraint mask are adopted to enable our tracker to be robust to various appearance changes. Extensive experiments have been done to prove the effectiveness of our method.

@InProceedings{Liu_2015_CVPR,author = {Liu, Ting and Wang, Gang and Yang, Qingxiong},title = {Real-Time Part-Based Visual Tracking via Adaptive Correlation Filters},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel Hilbert space (RKHS).  Our new perspective is based on viewing the steps of the KLSH algorithm in an appropriately projected space, and has several key theoretical and practical benefits.  First, it eliminates the problematic conceptual difficulties that are present in the existing motivation of KLSH.  Second, it yields the first formal retrieval performance bounds for KLSH.  Third, our analysis reveals two techniques for boosting the empirical performance of KLSH.  We evaluate these extensions on several large-scale benchmark image retrieval data sets, and show that our analysis leads to improved recall performance of at least 12%, and sometimes much higher, over the standard KLSH method.

@InProceedings{Jiang_2015_CVPR,author = {Jiang, Ke and Que, Qichao and Kulis, Brian},title = {Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, Thomas G. Dietterich

Abstract: The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.  At prediction time, this approach must solve an often-challenging optimization problem.  Search-based methods provide an alternative that has the potential to achieve higher performance.  These methods learn to control a search procedure that constructs and evaluates candidate solutions.  The recently-developed HC-Search method has been shown to achieve state-of-the-art results in natural language processing, but mixed success when applied to vision problems.  This paper studies whether HC-Search can achieve similarly competitive performance on basic vision tasks such as object detection, scene labeling, and monocular depth estimation, where the leading paradigm is energy minimization.  To this end, we introduce a search operator suited to the vision domain that improves a candidate solution by probabilistically sampling likely object configurations in the scene from the hierarchical Berkeley segmentation.  We complement this search operator by applying the DAgger algorithm to robustly train the search heuristic so it learns from its previous mistakes.  Our evaluation shows that these improvements reduce the branching factor and search depth, and thus give a significant performance boost.  Our state-of-the-art results on scene labeling and depth estimation suggest that HC-Search provides a suitable tool for learning and inference in vision.

@InProceedings{Lam_2015_CVPR,author = {Lam, Michael and Rao Doppa, Janardhan and Todorovic, Sinisa and Dietterich, Thomas G.},title = {HC-Search for Structured Prediction in Computer Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Object recognition systems have shown great progress over recent years. However, creating object representations that are robust to changes in viewpoint while capturing local visual details continues to be a challenge. In particular, recent convolutional architectures employ spatial pooling to achieve scale and shift invariances, but they are still sensitive to out-of-plane rotations. In this paper, we formulate a probabilistic framework for analyzing the performance of pooling. This framework suggests two directions for improvement. First, we apply multiple scales of filters coupled with different pooling granularities, and second we make use of color as an additional pooling domain, thereby reducing the sensitivity to spatial deformations. We evaluate our algorithm on the object instance recognition task using two independent publicly available RGB-D datasets, and demonstrate significant improvements over the current state-of-the-art. In addition, we present a new dataset for industrial objects to further validate the effectiveness of our approach versus other state-of-the-art approaches for object recognition using RGB-D data.

@InProceedings{Li_2015_CVPR,author = {Li, Chi and Reiter, Austin and Hager, Gregory D.},title = {Beyond Spatial Pooling: Fine-Grained Representation Learning in Multiple Domains},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Lizhi Wang, Zhiwei Xiong, Dahua Gao, Guangming Shi, Wenjun Zeng, Feng Wu

Abstract: We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. Our work has two key technical contributions. First, we build a dual-camera system that simultaneously captures a panchromatic video at a high frame rate and a hyperspectral video at a low frame rate, which jointly provide reliable projections for the underlying HSHS video. Second, we exploit the panchromatic video to learn an over-complete 3D dictionary to represent each band-wise video sparsely, and a robust computational reconstruction is then employed to recover the HSHS video based on the joint videos and the self-learned dictionary. Experimental results demonstrate that, for the first time to our knowledge, the hyperspectral video frame rate reaches up to 100fps with decent quality, even when the incident light is not strong.

@InProceedings{Wang_2015_CVPR,author = {Wang, Lizhi and Xiong, Zhiwei and Gao, Dahua and Shi, Guangming and Zeng, Wenjun and Wu, Feng},title = {High-Speed Hyperspectral Video Acquisition With a Dual-Camera Architecture},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Masoud Faraki, Mehrtash T. Harandi, Fatih Porikli

Abstract: This paper takes a step forward in image and video coding by extending the well-known Vector of Locally Aggregated Descriptors (VLAD) onto an extensive space of curved Riemannian manifolds. We provide a comprehensive mathematical framework that formulates the aggregation problem of such manifold data into an elegant solution. In particular, we consider structured descriptors from visual data, namely Region Covariance Descriptors and linear subspaces that reside on the manifold of Symmetric Positive Definite matrices and the Grassmannian manifolds, respectively. Through rigorous experimental validation, we demonstrate the superior performance of this novel Riemannian VLAD descriptor on several visual classification tasks including video-based face recognition, dynamic scene recognition, and head pose classification.

@InProceedings{Faraki_2015_CVPR,author = {Faraki, Masoud and Harandi, Mehrtash T. and Porikli, Fatih},title = {More About VLAD: A Leap From Euclidean to Riemannian Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ali Mosleh, Paul Green, Emmanuel Onzon, Isabelle Begin, J.M. Pierre Langlois

Abstract: This paper presents a reliable non-blind method to measure intrinsic lens blur. We first introduce an accurate camera-scene alignment framework that avoids erroneous homography estimation and camera tone curve estimation. This alignment is used to generate a sharp correspondence of a target pattern captured by the camera. Second, we introduce a Point Spread Function (PSF) estimation approach where information about the frequency spectrum of the target image is taken into account. As a result of these steps and the ability to use multiple target images in this framework, we achieve a PSF estimation method robust against noise and suitable for mobile devices. Experimental results show that the proposed method results in PSFs with more than 10 dB higher accuracy in noisy conditions compared with the PSFs generated using state-of-the-art techniques.

@InProceedings{Mosleh_2015_CVPR,author = {Mosleh, Ali and Green, Paul and Onzon, Emmanuel and Begin, Isabelle and Pierre Langlois, J.M.},title = {Camera Intrinsic Blur Kernel Estimation: A Reliable Framework},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jingjing Xiao, Rustam Stolkin, Ales Leonardis

Abstract: This paper presents a method for single target tracking of arbitrary objects in challenging video sequences. Targets are modeled at three different levels of granularity (pixel level, parts-based level and bounding box level), which are cross-constrained to enable robust model relearning. The main contribution is an adaptive clustered decision tree method which dynamically selects the minimum combination of features necessary to sufficiently represent each target part at each frame, thereby providing robustness with computational efficiency. The adaptive clustered decision tree is implemented in two separate parts of the tracking algorithm: firstly to enable robust matching at the parts-based level between successive frames; and secondly to select the best superpixels for learning new parts of the target. We have tested the tracker using two different tracking benchmarks (VOT2013-2014 and CVPR2013 tracking challenges), based on two different test methodologies, and show it to be significantly more robust than the best state-of-the-art methods from both of those tracking challenges, while also offering competitive tracking precision.

@InProceedings{Xiao_2015_CVPR,author = {Xiao, Jingjing and Stolkin, Rustam and Leonardis, Ales},title = {Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-Level Appearance Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Traditional data-driven classifier learning approaches become limited when the training data is inadequate either in quantity or quality. To address this issue, in this paper we propose to combine hidden information and data to enhance classifier learning. Hidden information represents information that is only available during training but not available during testing. It often exists in many applications yet has not been thoroughly exploited, and existing methods to utilize hidden information are still limited. To this end, we propose two general approaches to exploit different types of hidden information to improve different classifiers. We also extend the proposed methods to deal with incomplete hidden information. Experimental results on different applications demonstrate the effectiveness of the proposed methods for exploiting hidden information and their superior performance to existing methods.

@InProceedings{Wang_2015_CVPR,author = {Wang, Ziheng and Ji, Qiang},title = {Classifier Learning With Hidden Information},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhuwen Li, Ping Tan, Robby T. Tan, Danping Zou, Steven Zhiying Zhou, Loong-Fah Cheong

Abstract: We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We first improve the photo-consistency term to explicitly model the appearance change due to the scattering effects. The prior matting Laplacian constraint on fog transmission imposes a detail-preserving smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transmission at neighboring points. These novel constraints are formulated together in an MRF framework, which is optimized iteratively by introducing auxiliary variables. The experiment results on real videos demonstrate the strength of our method.

@InProceedings{Li_2015_CVPR,author = {Li, Zhuwen and Tan, Ping and Tan, Robby T. and Zou, Danping and Zhiying Zhou, Steven and Cheong, Loong-Fah},title = {Simultaneous Video Defogging and Stereo Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang

Abstract: We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. The unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches; and ii) improves the robustness in coping with large pose variations. The framework demonstrates real-time performance and state-of-theart results on various benchmarks including the challenging 300-W dataset.

@InProceedings{Zhu_2015_CVPR,author = {Zhu, Shizhan and Li, Cheng and Change Loy, Chen and Tang, Xiaoou},title = {Face Alignment by Coarse-to-Fine Shape Searching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tsung-Yi Lin, Yin Cui, Serge Belongie, James Hays

Abstract: The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or "bird's eye" imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned cross- view image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross- view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.

@InProceedings{Lin_2015_CVPR,author = {Lin, Tsung-Yi and Cui, Yin and Belongie, Serge and Hays, James},title = {Learning Deep Representations for Ground-to-Aerial Geolocalization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper, we propose a novel unsupervised feature selection method: Simultaneous Orthogonal basis Clustering Feature Selection (SOCFS). To perform feature selection on unlabeled data effectively, a regularized regression-based formulation with a new type of target matrix is designed. The target matrix captures latent cluster centers of the projected data points by performing orthogonal basis clustering, and then guides the projection matrix to select discriminative features. Unlike the recent unsupervised feature selection methods, SOCFS does not explicitly use the pre-computed local structure information for data points represented as additional terms of their objective functions, but directly computes latent cluster information by the target matrix conducting orthogonal basis clustering in a single unified term of the proposed objective function. It turns out that the proposed objective function can be minimized by a simple optimization algorithm. Experimental results demonstrate the effectiveness of SOCFS achieving the state-of-the-art results with diverse real world datasets.

@InProceedings{Han_2015_CVPR,author = {Han, Dongyoon and Kim, Junmo},title = {Unsupervised Simultaneous Orthogonal Basis Clustering Feature Selection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}