Xing Mei, Weiming Dong, Bao-Gang Hu, Siwei Lyu

Abstract: Marginal histograms provide valuable information for various computer vision problems. However, current image restoration methods do not fully exploit the potential of marginal histograms, in particular, their role as ensemble constraints on the marginal statistics of the restored image. In this paper, we introduce a new framework, UniHIST, to incorporate marginal histogram constraints into image restoration. The key idea of UniHIST is to minimize the discrepancy between the marginal histograms of the restored image and the reference histograms in pixel or gradient domains using the quadratic Wasserstein (W2) distance. The W2 distance can be computed directly from data without resorting to density estimation. It provides a differentiable metric between marginal histograms and allows easy integration with existing image restoration methods. We demonstrate the effectiveness of UniHIST through denoising of pattern images and non-blind deconvolution of natural images. We show that UniHIST enhances restoration performance and leads to visual and quantitative improvements over existing state-of-the-art methods.

@InProceedings{Mei_2015_CVPR,author = {Mei, Xing and Dong, Weiming and Hu, Bao-Gang and Lyu, Siwei},title = {UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Detailed analysis of human action, such as action classification, detection and  localization has received increasing attention from the community; datasets like JHMDB have made it plausible to conduct studies analyzing the impact that such deeper information has on the greater action understanding problem.  However, detailed automatic segmentation of human action has comparatively been unexplored.  In this paper, we take a step in that direction and propose a hierarchical MRF model to bridge low-level video fragments with high-level human motion and appearance; novel higher-order potentials connect different levels of the supervoxel hierarchy to enforce the consistency of the human segmentation by pulling from different segment-scales.  Our single layer model significantly outperforms the current state-of-the-art on actionness,  and our full model improves upon the single layer baselines in action segmentation.

@InProceedings{Lu_2015_CVPR,author = {Lu, Jiasen and Xu, ran and Corso, Jason J.},title = {Human Action Segmentation With Hierarchical Supervoxel Consistency},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Bernard Ghanem, Ali Thabet, Juan Carlos Niebles, Fabian Caba Heilbron

Abstract: This paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image. Our technique formulates this problem as the estimation of a rotation matrix that best aligns the normals of the captured scene to a canonical world axes. By introducing sparsity constraints, our method can simultaneously estimate the scene MF, the surfaces in the scene that are best aligned to one of three  coordinate axes, and the outlier surfaces that do not align with  any of the axes. To test our approach, we contribute a new set of annotations to determine ground truth MFs in each image of the popular NYUv2 dataset. We use this new benchmark to experimentally demonstrate that our method is more accurate, faster, more reliable and more robust than the methods used in the literature. We further motivate our technique by showing how it can be used to address the RGB-D SLAM problem in indoor scenes by incorporating it into and improving the performance of a popular RGB-D SLAM method.

@InProceedings{Ghanem_2015_CVPR,author = {Ghanem, Bernard and Thabet, Ali and Carlos Niebles, Juan and Caba Heilbron, Fabian},title = {Robust Manhattan Frame Estimation From a Single RGB-D Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jia Xu, Alexander G. Schwing, Raquel Urtasun

Abstract: Despite the promising performance of conventional fully supervised  algorithms,  semantic segmentation has remained an important, yet challenging task. Due to the limited availability of complete annotations, it is of great interest to design solutions for semantic segmentation that take into account weakly labeled data, which is readily available at a much larger scale. Contrasting the common theme to develop a different algorithm for each type of weak annotation, in this work, we propose a unified approach that incorporates various forms of weak supervision -- image level tags, bounding boxes, and partial labels -- to produce a pixel-wise labeling. We conduct a rigorous evaluation on the challenging Siftflow dataset for various weakly labeled settings, and show that our approach outperforms  the state-of-the-art by $12\%$ on per-class accuracy, while maintaining comparable per-pixel accuracy.

@InProceedings{Xu_2015_CVPR,author = {Xu, Jia and Schwing, Alexander G. and Urtasun, Raquel},title = {Learning to Segment Under Various Forms of Weak Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Samuel Schulter, Christian Leistner, Horst Bischof

Abstract: The aim of single image super-resolution is to reconstruct a high-resolution image from a single low-resolution input. Although the task is ill-posed it can be seen as finding a non-linear mapping from a low to high-dimensional space. Recent methods that rely on both neighborhood embedding and sparse-coding have led to tremendous quality improvements. Yet, many of the previous approaches are hard to apply in practice because they are either too slow or demand tedious parameter tweaks.   In this paper, we propose to directly map from low to high-resolution patches using random forests. We show the close relation of previous work on single image super-resolution to locally linear regression and demonstrate how random forests nicely fit into this framework. During training the trees, we optimize a novel and effective regularized objective that not only operates on the output space but also on the input space, which especially suits the regression task. During inference, our method comprises the same well-known computational efficiency that has made random forests popular for many computer vision problems.   In the experimental part, we demonstrate on standard benchmarks for single image super-resolution that our approach yields highly accurate state-of-the-art results, while being fast in both training and evaluation.

@InProceedings{Schulter_2015_CVPR,author = {Schulter, Samuel and Leistner, Christian and Bischof, Horst},title = {Fast and Accurate Image Upscaling With Super-Resolution Forests},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We present a novel phase-based approach for recon- structing 4D light field from a micro-baseline stereo (LfS) pair. Our approach takes advantage of the unique prop- erty of complex steerable pyramid filters in micro-baseline stereo. We first introduce a disparity assisted phase based synthesis (DAPS) strategy that can integrate disparity infor- mation into the phase term of a reference image to warp it to its close neighbor views. Based on the DAPS, an "analy- sis by synthesis" approach is proposed to warp from one of the input binocular images to the other, and iteratively op- timize the disparity map to minimize the phase differences between the warped one and the ground truth input. Fi- nally, the densely and regularly spaced, high quality light field images can be reconstructed using the proposed DAPS according to the refined disparity map. Our approach also solves the problems of disparity inconsistency and ringing artifact in available phase-based view synthesis methods. Experimental results demonstrate that our approach sub- stantially improves both the quality of disparity map and light field, compared with the state-of-the-art stereo match- ing and image based rendering approaches.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Zhoutong and Liu, Yebin and Dai, Qionghai},title = {Light Field From Micro-Baseline Image Pair},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ahmed Elhayek, Edilson de Aguiar, Arjun Jain, Jonathan Tompson, Leonid Pishchulin, Micha Andriluka, Chris Bregler, Bernt Schiele, Christian Theobalt

Abstract: We present a novel method for accurate marker-less capture of articulated skeleton motion of several subjects in general scenes, indoors and outdoors, even from input filmed with as few as two cameras. Our approach unites a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through a combined pose optimization energy. The discriminative part-based pose detection method, implemented using Convolutional Networks (ConvNet), estimates unary potentials for each joint of a kinematic skeleton model. These unary potentials are used to probabilistically extract pose constraints for tracking by using weighted sampling from a pose posterior guided by the model. In the final energy, these constraints are combined with an appearance-based model-to-image similarity term. Poses can be computed very efficiently using iterative local optimization, as ConvNet detection is fast, and our formulation yields a combined pose estimation energy with analytic derivatives. In combination, this enables to track full articulated joint angles at state-of-the-art accuracy and temporal stability with a very low number of cameras.

@InProceedings{Elhayek_2015_CVPR,author = {Elhayek, Ahmed and de Aguiar, Edilson and Jain, Arjun and Tompson, Jonathan and Pishchulin, Leonid and Andriluka, Micha and Bregler, Chris and Schiele, Bernt and Theobalt, Christian},title = {Efficient ConvNet-Based Marker-Less Motion Capture in General Scenes With a Low Number of Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi

Abstract: Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, \dcnn, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. \dcnn substantially improves the state-of-the-art in texture, material and scene recognition. Our approach achieves 79.8\% accuracy on Flickr material dataset and 81\% accuracy on MIT indoor scenes, providing absolute gains of more than 10\% over existing approaches. \dcnn easily transfers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, \dcnn can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at localizing ``stuff'' categories and obtains state-of-the-art results on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.

@InProceedings{Cimpoi_2015_CVPR,author = {Cimpoi, Mircea and Maji, Subhransu and Vedaldi, Andrea},title = {Deep Filter Banks for Texture Recognition and Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hironori Hattori, Vishnu Naresh Boddeti, Kris M. Kitani, Takeo Kanade

Abstract: We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pedestrian motion. We propose an efficient discriminative learning method that generates a spatially-varying pedestrian appearance model that takes into the account the perspective geometry of the scene. As a result, our method is able to learn a unique pedestrian classifier customized for every possible location in the scene. Our experimental results show that our proposed approach outperforms classical pedestrian detection models and hybrid synthetic-real models. Our results also yield a surprising result, that our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited.

@InProceedings{Hattori_2015_CVPR,author = {Hattori, Hironori and Naresh Boddeti, Vishnu and Kitani, Kris M. and Kanade, Takeo},title = {Learning Scene-Specific Pedestrian Detectors Without Real Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chulwoo Lee, Won-Dong Jang, Jae-Young Sim, Chang-Su Kim

Abstract: A graph-based system to simulate the movements and interactions of multiple random walkers (MRW) is proposed in this work. In the MRW system, multiple agents traverse a single graph simultaneously. To achieve desired interactions among those agents, a restart rule can be designed, which determines the restart distribution of each agent according to the probability distributions of all agents. In particular, we develop the repulsive rule for data clustering. We illustrate that the MRW clustering can segment real images reliably. Furthermore, we propose a novel image cosegmentation algorithm based on the MRW clustering. Specifically, the proposed algorithm consists of two steps: inter-image concurrence computation and intra-image MRW clustering. Experimental results demonstrate that the proposed algorithm provides promising cosegmentation performance.

@InProceedings{Lee_2015_CVPR,author = {Lee, Chulwoo and Jang, Won-Dong and Sim, Jae-Young and Kim, Chang-Su},title = {Multiple Random Walkers and Their Application to Image Cosegmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Rui Caseiro, Joao F. Henriques, Pedro Martins, Jorge Batista

Abstract: Recently, a particular paradigm [9] in the domain adaptation field has received considerable attention by introducing novel and important insights to the problem. In this case, the source/target domains are represented in the form of subspaces, which are treated as points on the Grassmann manifold. The geodesic curve between them is sampled to obtain intermediate points. Then a classifier is learnt using the projections of the data onto these subspaces. Despite its relevance and popularity, this paradigm [9] contains some limitations. Firstly, in real-world applications, that simple curve (i.e. shortest path) does not provide the necessary flexibility to model the domain shift between the training and testing data sets. Secondly, by using the geodesic curve, we are restricted to only one source domain, which does not allow to take fully advantage of the multiple datasets that are available nowadays. It is then, natural to ask whether this popular paradigm could be extended to deal with more complex curves (e.g. splines) and to integrate multi-sources domains. This is a hard problem considering the Riemannian structure of the space, but we propose a mathematically well-founded idea that enables us to solve it.  We exploit the geometric insight of rolling maps [14] to compute a spline curve on the Grassmann manifold. The benefits of the proposed idea are demonstrated through several empirical studies on standard datasets. This novel paradigm allows to explicitly integrate multi-source domains while the previous one [9] uses the mean of all sources. This enables to model better the domain shift and take fully advantage of the training datasets.

@InProceedings{Caseiro_2015_CVPR,author = {Caseiro, Rui and Henriques, Joao F. and Martins, Pedro and Batista, Jorge},title = {Beyond the Shortest Path : Unsupervised Domain Adaptation by Sampling Subspaces Along the Spline Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang

Abstract: With benefits of low storage costs and high query speeds, hashing methods are widely researched for efficiently retrieving large-scale data, which commonly contains multiple views, e.g. a news report with images, videos and texts. In this paper, we study the problem of cross-view retrieval and propose an effective Semantics-Preserving Hashing method, termed SePH. Given semantic affinities of training data as supervised information, SePH transforms them into a probability distribution and approximates it with to-be-learnt hash codes in Hamming space via minimizing the Kullback-Leibler divergence. Then kernel logistic regression with a sampling strategy is utilized to learn the nonlinear projections from features in each view to the learnt hash codes. And for any unseen instance, predicted hash codes and their corresponding output probabilities from observed views are utilized to determine its unified hash code, using a novel probabilistic approach. Extensive experiments conducted on three benchmark datasets well demonstrate the effectiveness and reasonableness of SePH.

@InProceedings{Lin_2015_CVPR,author = {Lin, Zijia and Ding, Guiguang and Hu, Mingqing and Wang, Jianmin},title = {Semantics-Preserving Hashing for Cross-View Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Etai Littwin, Hadar Averbuch-Elor, Daniel Cohen-Or

Abstract: In this paper, we introduce a spherical embedding technique to position a given set of silhouettes of an object as observed from a set of cameras arbitrarily positioned around the object.  Our technique estimates dissimilarities among the silhouettes and embeds them directly in the rotation space SO(3). The embedding is obtained by an optimization scheme applied over the rotations represented with exponential maps.  Since the measure for inter-silhouette dissimilarities contains many outliers, our key idea is to perform the embedding by only using a subset of the estimated dissimilarities. We present a technique that carefully screens for inlier-distances, and the pairwise scaled dissimilarities are embedded in a spherical space, diffeomorphic to SO(3). We show that our method outperforms spherical MDS embedding, demonstrate its performance on various multi-view sets, and highlight its robustness to outliers.

@InProceedings{Littwin_2015_CVPR,author = {Littwin, Etai and Averbuch-Elor, Hadar and Cohen-Or, Daniel},title = {Spherical Embedding of Inlier Silhouette Dissimilarities},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chaoyang Wang, Long Zhao, Shuang Liang, Liqing Zhang, Jinyuan Jia, Yichen Wei

Abstract: Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm. However, standard single-way hierarchical methods are fundamentally flawed in that the errors in early steps cannot be corrected and accumulate. In this work, we propose a novel multi-branch hierarchical segmentation approach that alleviates such problems by learning multiple merging strategies in each step in a complementary manner, such that errors in one merging strategy could be corrected by the others. Our approach achieves the state-of-the-art performance for both object proposal and object detection tasks, comparing to previous object proposal methods.

@InProceedings{Wang_2015_CVPR,author = {Wang, Chaoyang and Zhao, Long and Liang, Shuang and Zhang, Liqing and Jia, Jinyuan and Wei, Yichen},title = {Object Proposal by Multi-Branch Hierarchical Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wei Yang, Yu Ji, Haiting Lin, Yang Yang, Sing Bing Kang, Jingyi Yu

Abstract: There has been emerging interest on recovering traditionally challenging intrinsic scene properties. In this paper, we present a novel computational imaging solution for recovering the ambient occlusion (AO) map of an object. AO measures how much light from all different directions can reach a surface point without being blocked by self-occlusions. Previous approaches either require obtaining highly accurate surface geometry or acquiring a large number of images. We adopt a compressive sensing framework that captures the object under strategically coded lighting directions. We show that this incident illumination field exhibits some unique properties suitable for AO recovery: every ray's contribution to the visibility function is binary while their distribution for AO measurement is sparse. This enables a sparsity-prior based solution for iteratively recovering the surface normal, the surface albedo, and the visibility function from a small number of images. To physically implement the scheme, we construct an encodable directional light source using the light field probe. Experiments on synthetic and real scenes show that our approach is both reliable and accurate with significantly reduced size of input.

@InProceedings{Yang_2015_CVPR,author = {Yang, Wei and Ji, Yu and Lin, Haiting and Yang, Yang and Bing Kang, Sing and Yu, Jingyi},title = {Ambient Occlusion via Compressive Visibility Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Naeemullah Khan, Marei Algarni, Anthony Yezzi, Ganesh Sundaramoorthi

Abstract: We propose new dense descriptors for texture segmentation. Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients. These scale spaces are defined by Poisson-like partial differential equations. A key property of our new descriptors is that they do not aggregate image data across the boundary of the region, in contrast to existing descriptors based on aggregation of oriented gradients. As an example, we show how the descriptor can be incorporated in a Mumford-Shah energy for texture segmentation. We test our method on several challenging datasets for texture segmentation and textured object tracking. Experiments indicate that our descriptors lead to more accurate segmentation than non-shape dependent descriptors and the state-of-the-art in texture segmentation.

@InProceedings{Khan_2015_CVPR,author = {Khan, Naeemullah and Algarni, Marei and Yezzi, Anthony and Sundaramoorthi, Ganesh},title = {Shape-Tailored Local Descriptors and Their Application to Segmentation and Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this work we propose a method for simultaneously learning features and a corresponding similarity metric for person re-identification. We present a deep convolution architecture with layers specially designed to address the problem of re-identification. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. Novel elements of our architecture include a layer that computes cross-input neighborhood differences, which capture local relationships among mid-level features that were computed separately from the two input images. A high-level summary of the outputs of this layer is computed by a layer of patch summary features, which are then spatially integrated in subsequent layers. Our method significantly outperforms the state of the art on both a large data set (CUHK03) and a medium-sized dataset (CUHK01), and it is resistant to overfitting. We also demonstrate that by initially training on an unrelated large data set before fine tuning on a small target data set, our network can achieve results comparable to the state of the art even on the small data set (VIPeR).

@InProceedings{Ahmed_2015_CVPR,author = {Ahmed, Ejaz and Jones, Michael and Marks, Tim K.},title = {An Improved Deep Learning Architecture for Person Re-Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mayank Kabra, Alice Robie, Kristin Branson

Abstract: Modern supervised learning algorithms can learn very accurate and complex discriminating functions. But when these classifiers fail, this complexity can also be a drawback because there is no easy, intuitive way to diagnose why they are failing and remedy the problem. This important question has received little attention. To address this problem, we propose a novel method to analyze and understand a classifier's errors. Our method centers around a measure of how much influence a training example has on the classifier's prediction for a test example. To understand why a classifier is mispredicting the label of a given test example, the user can find and review the most influential training examples that caused this misprediction, allowing them to focus their attention on relevant areas of the data space. This will aid the user in determining if and how the training data is inconsistently labeled or lacking in diversity, or if the feature representation is insufficient. As computing the influence of each training example is computationally impractical, we propose a novel distance metric to approximate influence for boosting classifiers that is fast enough to be used interactively. We also show several novel use paradigms of our distance metric. Through experiments, we show that it can be used to find incorrectly or inconsistently labeled training examples, to find specific areas of the data space that need more training data, and to gain insight into which features are missing from the current representation.

@InProceedings{Kabra_2015_CVPR,author = {Kabra, Mayank and Robie, Alice and Branson, Kristin},title = {Understanding Classifier Errors by Examining Influential Neighbors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ting-Hsuan Chao, Yen-Liang Lin, Yin-Hsi Kuo, Winston H. Hsu

Abstract: For practical applications, an object detection system requires huge number of classes to meet real world needs. Many successful object detection systems use part-based model which trains several filters (classifiers) for each class to perform multiclass object detection. However, these methods have linear computational complexity in regard to the number of classes and may lead to huge computing time. To solve the problem, some works learn a codebook for the filters and conduct operations only on the codebook to make computational complexity sublinear in regard to the number of classes. But the past studies missed to consider filter characteristics, e.g., filters are weights trained by Support Vector Machine, and rather they applied method such as sparse coding for visual signals' optimization. This misuse results in huge accuracy loss when a large speedup is required. To remedy this shortcoming, we have developed a new method called Regularized Sparse Coding which is designed to reconstruct filter functionality. That is, it reconstructs the ability of filter to produce accurate score for classification. Our method can reconstruct filters by minimizing score map error, while sparse coding reconstructs filters by minimizing appearance error. This different optimization strategy makes our method be able to have small accuracy loss when a large speedup is achieved. On the ILSVRC 2013 dataset, which has 200 classes, this work represents a 16 times speedup using only 1.25% memory on single CPU with 0.04 mAP drop when compared with the original Deformable Part Model. Moreover, parallel computing on GPUs is also applicable for our work to achieve more speedup.

@InProceedings{Chao_2015_CVPR,author = {Chao, Ting-Hsuan and Lin, Yen-Liang and Kuo, Yin-Hsi and Hsu, Winston H.},title = {Scalable Object Detection by Filter Compression With Regularized Sparse Coding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: While sparse coding on non-flat Riemannian manifolds has recently become increasingly popular, existing solutions either are dedicated to specific manifolds, or rely on optimization problems that are difficult to solve, especially when it comes to dictionary learning. In this paper, we propose to make use of kernels to perform coding and dictionary learning on Riemannian manifolds. To this end, we introduce a general Riemannian coding framework with its kernel-based counterpart. This lets us (i) generalize beyond the special case of sparse coding; (ii) introduce efficient solutions to two coding schemes; (iii) learn the kernel parameters; (iv) perform unsupervised and supervised dictionary learning in a much simpler manner than previous Riemannian coding methods. We demonstrate the effectiveness of our approach on three different types of non-flat manifolds, and illustrate its generality by applying it to Euclidean spaces, which also are Riemannian manifolds.

@InProceedings{Harandi_2015_CVPR,author = {Harandi, Mehrtash and Salzmann, Mathieu},title = {Riemannian Coding and Dictionary Learning: Kernels to the Rescue},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Benjamin Resch, Hendrik P. A. Lensch, Oliver Wang, Marc Pollefeys, Alexander Sorkine-Hornung

Abstract: Videos consisting of thousands of high resolution frames are challenging for existing structure from motion (SfM) and simultaneous-localization and mapping (SLAM) techniques. We present a new approach for simultaneously computing extrinsic camera poses and 3D scene structure that is capable of handling such large volumes of image data. The key insight behind this paper is to effectively exploit coherence in densely sampled video input. Our technical contributions include robust tracking and selection of confident video frames, a novel window bundle adjustment, frame-to-structure verification for globally consistent reconstructions with multi-loop closing, and utilizing efficient global linear camera pose estimation in order to link both consecutive and distant bundle adjustment windows. To our knowledge we describe the first system that is capable of handling high resolution, high frame-rate video data with close to realtime performance. In addition, our approach can robustly integrate data from different video sequences, allowing multiple video streams to be simultaneously calibrated in an efficient and globally optimal way. We demonstrate high quality alignment on large scale challenging datasets, e.g., 2-20 megapixel resolution at frame rates of 25-120 Hz with thousands of frames.

@InProceedings{Resch_2015_CVPR,author = {Resch, Benjamin and Lensch, Hendrik P. A. and Wang, Oliver and Pollefeys, Marc and Sorkine-Hornung, Alexander},title = {Scalable Structure From Motion for Densely Sampled Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model. We call each connected subtree a flexible composition of object parts. This involves a novel method for learning occlusion cues. During inference we need to search over a mixture of different flexible models. By exploiting part sharing, we show that this inference can be done extremely efficiently requiring only twice as many computations as searching for the entire object (i.e., not modeling occlusion). We evaluate our model on the standard benchmarked "We Are Family" Stickmen dataset and obtain significant performance improvements over the best alternative algorithms.

@InProceedings{Chen_2015_CVPR,author = {Chen, Xianjie and Yuille, Alan L.},title = {Parsing Occluded People by Flexible Compositions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Shenlong Wang, Sanja Fidler, Raquel Urtasun

Abstract: In this paper we are interested in exploiting geographic priors to help outdoor scene understanding. Towards this goal we propose  a holistic approach that reasons jointly about 3D object detection, pose estimation, semantic segmentation as well as depth reconstruction from a single image. Our approach takes  advantage of  large-scale crowd-sourced maps to generate dense geographic, geometric and semantic priors by rendering the 3D world. We demonstrate the effectiveness of our holistic model on the challenging KITTI dataset, and show significant improvements over the baselines in all metrics and tasks.

@InProceedings{Wang_2015_CVPR,author = {Wang, Shenlong and Fidler, Sanja and Urtasun, Raquel},title = {Holistic 3D Scene Understanding From a Single Geo-Tagged Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Davide Modolo, Alexander Vezhnevets, Olga Russakovsky, Vittorio Ferrari

Abstract: We present a method for calibrating the Ensemble of Exemplar SVMs model. Unlike the standard approach, which calibrates each SVM independently, our method optimizes their joint performance as an ensemble. We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum. The algorithm dynamically discards parts of the solution space that cannot contain the optimum early on, making the optimization computationally feasible. We experiment with EE-SVM trained on state-of-the-art CNN descriptors. Results on the ILSVRC 2014 and PASCAL VOC 2007 datasets show that (i) our joint calibration procedure outperforms independent calibration on the task of classifying windows as belonging to an object class or not; and (ii) this improved window classifier leads to better performance on the object detection task.

@InProceedings{Modolo_2015_CVPR,author = {Modolo, Davide and Vezhnevets, Alexander and Russakovsky, Olga and Ferrari, Vittorio},title = {Joint Calibration of Ensemble of Exemplar SVMs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang

Abstract: This paper aims to highlight vision related tasks centered around "car", which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, "CompCars", that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillancenature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html

@InProceedings{Yang_2015_CVPR,author = {Yang, Linjie and Luo, Ping and Change Loy, Chen and Tang, Xiaoou},title = {A Large-Scale Car Dataset for Fine-Grained Categorization and Verification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang

Abstract: Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition. The mainstream works to address this problem focus on designing engineered gradient features. In this work, we show that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs). While rather than using the networks as a blackbox feature extractor, we customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters. A new loss function, named positive-sharing loss, in which each subclass shares the loss for the whole positive class, is proposed to learn the parameters. Compared to the sofmax loss function, the proposed one, introduces an extra regularizer to emphasizes the losses for the positive and negative classes, which facilitates to explore more discriminative features. Our experimental results demonstrate that learned deep features can achieve top performance on Berkeley Segmentation Dataset and Benchmark (BSDS500) and obtain competitive cross dataset generalization result on the NYUD dataset.

@InProceedings{Shen_2015_CVPR,author = {Shen, Wei and Wang, Xinggang and Wang, Yan and Bai, Xiang and Zhang, Zhijiang},title = {DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs). The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificial boundaries on the images and may impact the quality of the extracted features. Besides, the operations on the raw image domain require to compute thousands of networks on a single image, which is time-consuming.  In this paper, we propose to exploit shape information via masking convolutional features. The proposal segments (e.g., super-pixels) are treated as masks on the convolutional feature maps. The CNN features of segments are directly masked out from these maps and used to train classifiers for recognition. We further propose a joint method to handle objects and "stuff" (e.g., grass, sky, water) in the same framework. State-of-the-art results are demonstrated on the challenging PASCAL VOC benchmarks, with a compelling computational speed.

@InProceedings{Dai_2015_CVPR,author = {Dai, Jifeng and He, Kaiming and Sun, Jian},title = {Convolutional Feature Masking for Joint Object and Stuff Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light. We introduce a simple setup that allows us alter the incident light paths before light rays enter the object, and develop a method for recovering the object surface based on reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the shape of the object nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a complex shape and structure, with unknown and even inhomogeneous refractive index. Experimental results on both synthetic and real data are presented which demonstrate the feasibility and accuracy of our proposed approach.

@InProceedings{Han_2015_CVPR,author = {Han, Kai and Wong, Kwan-Yee K. and Liu, Miaomiao},title = {A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ayan Chakrabarti, Ying Xiong, Steven J. Gortler, Todd Zickler

Abstract: We introduce a multi-scale framework for low-level vision, where the goal is estimating physical scene values from image data---such as depth from stereo image pairs. The framework uses a dense, overlapping set of image regions at multiple scales and a ``local model,'' such as a slanted-plane model for stereo disparity, that is expected to be valid piecewise across the visual field. Estimation is cast as optimization over a dichotomous mixture of variables, simultaneously determining which regions are inliers with respect to the local model (binary variables) and the correct co-ordinates in the local model space for each inlying region (continuous variables). When the regions are organized into a multi-scale hierarchy, optimization can occur in an efficient and parallel architecture, where distributed computational units iteratively perform calculations and share information through  sparse connections between parents and children. The framework performs well on a standard benchmark for binocular stereo, and it produces a distributional scene representation that is appropriate for combining with higher-level reasoning and other low-level cues.

@InProceedings{Chakrabarti_2015_CVPR,author = {Chakrabarti, Ayan and Xiong, Ying and Gortler, Steven J. and Zickler, Todd},title = {Low-Level Vision by Consensus in a Spatial Hierarchy of Regions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jean-Dominique Favreau, Florent Lafarge, Adrien Bousseau

Abstract: Many design tasks involve the creation of new objects in the context of an existing scene. Existing work in computer vision only provides partial support for such tasks. On the one hand, multi-view stereo algorithms allow the reconstruction of real-world scenes, while on the other hand algorithms for line-drawing interpretation do not take context into account. Our work combines the strength of these two domains to interpret line drawings of imaginary objects drawn over photographs of an existing scene. The main challenge we face is to identify the existing 3D structure that correlates with the line drawing while also allowing the creation of new structure that is not present in the real world. We propose a labeling algorithm to tackle this problem, where some of the labels capture dominant orientations of the real scene while a free label allows the discovery of new orientations in the imaginary scene. We illustrate our algorithm by interpreting line drawings for urban planing, home remodeling, furniture design and cultural heritage.

@InProceedings{Favreau_2015_CVPR,author = {Favreau, Jean-Dominique and Lafarge, Florent and Bousseau, Adrien},title = {Line Drawing Interpretation in a Multi-View Context},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chun-Hao Huang, Edmond Boyer, Bibiana do Canto Angonese, Nassir Navab, Slobodan Ilic

Abstract: Human shape tracking consists in fitting a template model to temporal sequences of visual observations. It usually comprises an association step, that finds correspondences between the model and the input data, and a deformation step, that fits the model to the observations given correspondences. Most current approaches find their common ground with the Iterative-Closest-Point (ICP) algorithm, which facilitates the association step with local distance considerations. It fails when large deformations occur, and errors in the association tend to propagate over time. In this paper, we propose a discriminative alternative for the association, that leverages random forests to infer correspondences in one shot. It allows for large deformations and prevents tracking errors from accumulating. The approach is successfully integrated to a surface tracking framework that recovers human shapes and poses jointly. When combined with ICP, this discriminative association proves to yield better accuracy in registration, more stability when tracking over time, and faster convergence. Evaluations on existing datasets demonstrate the benefits with respect to the state-of-the-art.

@InProceedings{Huang_2015_CVPR,author = {Huang, Chun-Hao and Boyer, Edmond and do Canto Angonese, Bibiana and Navab, Nassir and Ilic, Slobodan},title = {Toward User-Specific Tracking by Detection of Human Shapes in Multi-Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Camera motion introduces motion blur, degrading the quality of  video. A video deblurring method is proposed based on two observations: (i) camera motion within capture of each individual frame leads to motion blur; (ii) camera motion between frames yields  inter-frame mis-alignment that can  be exploited for blur removal. The proposed method effectively leverages the information distributed across multiple video frames  due to camera motion, jointly estimating the motion between consecutive frames and blur within each frame. This joint analysis is crucial for achieving effective restoration by leveraging temporal information. Extensive experiments are carried out on  synthetic data as well as real-world blurry videos. Comparisons with several state-of-the-art methods verify the effectiveness of the proposed method.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Haichao and Yang, Jianchao},title = {Intra-Frame Deblurring by Leveraging Inter-Frame Camera Motion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Salient Object Subitizing

Jianming Zhang, Shugao Ma, Mehrnoosh Sameki, Stan Sclaroff, Margrit Betke, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech

Abstract: People can immediately and precisely identify 1, 2, 3 or 4 items by a simple glance. The phenomenon, known as Subitizing, inspires us to pursue the task of Salient Object Subitizing (SOS), i.e. predicting the existence and the number of salient objects in a scene using holistic cues. To study this problem, we propose a new image dataset  annotated by Amazon Mechanical Turk. We show that  for a substantial proportion of our dataset,  there is a high labeling consistency among different subjects, even when a very limited viewing time (0.5s) is given. On our dataset, the baseline method using the global Convolutional Neural Network (CNN) feature achieves 94% recall rate in detecting the existence of salient objects, and 42-82% recall rate  (chance is 20%) in predicting the number of salient objects (1, 2, 3, and 4+), without resorting to any object localization process. Finally, we demonstrate the usefulness of the proposed subitizing technique in two computer vision applications: salient object detection and object proposal.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Jianming and Ma, Shugao and Sameki, Mehrnoosh and Sclaroff, Stan and Betke, Margrit and Lin, Zhe and Shen, Xiaohui and Price, Brian and Mech, Radomir},title = {Salient Object Subitizing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Pose variation remains one of the major factors adversely affect the accuracy of real-world face recognition systems. Inspired by the recently proposed probabilistic elastic part (PEP) model and the success of the deep hierarchical architecture in a number of visual tasks, we propose the Hierarchical-PEP model to approach the unconstrained face recognition problem. We apply the PEP model hierarchically to decompose a face image into face parts at different levels of details to build pose-invariant part-based face representations. Following the hierarchy from bottom-up, we stack the face part representations at each layer, discriminatively reduce its dimensionality, and hence aggregate the face part representations layer-by-layer to build a compact and invariant face representation. The Hierarchical-PEP model exploits the fine-grained structures of the face parts at different levels of details to address the pose variations. It is also guided by supervised information in constructing the face part/face representations. We empirically verify the Hierarchical-PEP model on two public benchmarks (i.e., the LFW and YouTube Faces) and a face recognition challenge (i.e., the PaSC grand challenge) for image-based and video-based face verification. The state-of-the-art performance demonstrates the potential of our method.

@InProceedings{Li_2015_CVPR,author = {Li, Haoxiang and Hua, Gang},title = {Hierarchical-PEP Model for Real-World Face Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Haifei Huang, Hui Zhang, Yiu-ming Cheung

Abstract: In projective geometry, the common self-polar triangle has often been used to discuss the position relationship of two planar conics. However, there are few researches on the properties of the common self-polar triangle, especially when the two planar conics are special conics. In this paper, we explore the properties of the common self-polar triangle, when the two conics happen to be concentric circles. We show there exist infinite many common self-polar triangles of two concentric circles, and provide a method to locate the vertices of these triangles. By investigating all these triangles, we find that they encode two important properties. The first one is all triangles share one common vertex, and the opposite side of the common vertex lies on the same line, which are the circle center and the line at the infinity of the support plane. The second is all triangles are right triangles. Based on these two properties, the imaged circle center and the varnishing line of support plane can be recovered simultaneously, and many conjugate pairs on vanishing line can be obtained. These allow to induce good constraints on the image of absolute conic. We evaluate two calibration algorithms, whereby accurate results are achieved.  The main contribution of this paper is that we initiate a new perspective to look into circle-based camera calibration problem. We believe that other calibration methods using different circle patterns can benefit from this perspective, especially for the patterns which involve more than two circles.

@InProceedings{Huang_2015_CVPR,author = {Huang, Haifei and Zhang, Hui and Cheung, Yiu-ming},title = {The Common Self-Polar Triangle of Concentric Circles and Its Application to Camera Calibration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Taking a Deeper Look at Pedestrians

Jan Hosang, Mohamed Omran, Rodrigo Benenson, Bernt Schiele

Abstract: The only goal of the abstract is to the answer the question: why should I read this paper? In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architectural choices, parameters, and the influence of different training data, including pre-training on surrogate tasks.  We present the best convnet detectors on the Caltech and KITTI dataset. On Caltech our convnets reach top performance both for the Caltech1x and Caltech10x training setup. Using additional data at training time our strongest convnet model is competitive to detectors that use instead additional data at test time.

@InProceedings{Hosang_2015_CVPR,author = {Hosang, Jan and Omran, Mohamed and Benenson, Rodrigo and Schiele, Bernt},title = {Taking a Deeper Look at Pedestrians},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Katerina Fragkiadaki, Pablo Arbelaez, Panna Felsen, Jitendra Malik

Abstract: We segment  moving objects in  videos by ranking  spatio-temporal segment  proposals according to ``moving objectness'';  how likely they are to contain a moving  object.   In each video frame, we compute segment proposals   using multiple figure-ground segmentations on per frame motion  boundaries.  We rank them with a   Moving Objectness Detector  trained on  image and motion fields to detect moving objects and  discard over/under segmentations or background parts of the scene.  We extend the top ranked segments  into   spatio-temporal tubes using random walkers on  motion affinities of dense point trajectories.    Our final tube ranking consistently   outperforms previous segmentation methods in the two largest video segmentation benchmarks currently available,  for any number of proposals.   Further, our per frame moving object proposals increase  the  detection rate up to 7\% over  previous state-of-the-art static  proposal methods.

@InProceedings{Fragkiadaki_2015_CVPR,author = {Fragkiadaki, Katerina and Arbelaez, Pablo and Felsen, Panna and Malik, Jitendra},title = {Learning to Segment Moving Objects in Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Afshin Dehghan, Shayan Modiri Assari, Mubarak Shah

Abstract: Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art.

@InProceedings{Dehghan_2015_CVPR,author = {Dehghan, Afshin and Modiri Assari, Shayan and Shah, Mubarak},title = {GMMCP Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mingkui Tan, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Junbin Gao, Fuyuan Hu, Zhen Zhang

Abstract: Exploiting label dependency for multi-label image classification can significantly improve classification performance. Probabilistic Graphical Models are one of the primary methods for representing such dependencies. The structure of graphical models, however, is either determined heuristically or learned from very limited information. Moreover, neither of these approaches scales well to large or complex graphs. We propose a principled way to learn the structure of a graphical model by considering input features and labels, together with loss functions. We formulate this problem into a max-margin framework initially, and then transform it into a convex programming problem. Finally, we propose a highly scalable procedure that activates a set of cliques iteratively. Our approach exhibits both strong theoretical properties and a significant performance improvement over state-of-the-art methods on both synthetic and real-world data sets.

@InProceedings{Tan_2015_CVPR,author = {Tan, Mingkui and Shi, Qinfeng and van den Hengel, Anton and Shen, Chunhua and Gao, Junbin and Hu, Fuyuan and Zhang, Zhen},title = {Learning Graph Structure for Multi-Label Image Classification via Clique Generation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ching-Hui Chen, Vishal M. Patel, Rama Chellappa

Abstract: In real applications, data is not always explicitly-labeled. For instance, label ambiguity exists when we associate two persons appearing in a news photo with two names provided in the caption. We propose a matrix completion-based method for predicting the actual labels from the ambiguously labeled instances, and a standard supervised classifier can learn from the disambiguated labels to classify new data. We further generalize the method to handle the labeling constraints between instances when such prior knowledge is available. Compared to existing methods, our approach achieves 2.9% improvement on the labeling accuracy of the Lost dataset and comparable performance on the Labeled Yahoo! News dataset.

@InProceedings{Chen_2015_CVPR,author = {Chen, Ching-Hui and Patel, Vishal M. and Chellappa, Rama},title = {Matrix Completion for Resolving Label Ambiguity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mohamed Elgharib, Mohamed Hefeeda, Fredo Durand, William T. Freeman

Abstract: Video magnification reveals subtle variations that would be otherwise invisible to the naked eye. Current techniques require all motion in the video to be very small, which is unfortunately not always the case. Tiny yet meaningful motions are often combined with larger motions, such as the small vibrations of a gate as it rotates, or the microsaccades in a moving eye. We present a layer-based video magnification approach that can amplify small motions within large ones. An examined region/layer is temporally aligned and subtle variations are magnified. Matting is used to magnify only region of interest while maintaining integrity of nearby sites. Results show handling larger motions, larger amplification factors and significant reduction in artifacts over state of the art.

@InProceedings{Elgharib_2015_CVPR,author = {Elgharib, Mohamed and Hefeeda, Mohamed and Durand, Fredo and Freeman, William T.},title = {Video Magnification in Presence of Large Motions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Artem Rozantsev, Vincent Lepetit, Pascal Fua

Abstract: We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves.  Solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach to motion stabilization of local image patches that allows us to achieve effective classifica- tion on spatio-temporal image cubes and outperform state- of-the-art techniques.  As the problem is relatively new, we collected two chal- lenging datasets for UAVs and Aircrafts, which can be used as benchmarks for flying objects detection and vision- guided collision avoidance.

@InProceedings{Rozantsev_2015_CVPR,author = {Rozantsev, Artem and Lepetit, Vincent and Fua, Pascal},title = {Flying Objects Detection From a Single Moving Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mi Zhang, Jian Yao, Menghan Xia, Kai Li, Yi Zhang, Yaping Liu

Abstract: Fisheye image rectification and estimation of intrinsic parameters for real scenes have been addressed in the literature by using line information on the distorted images. In this paper, we propose an easily implemented fisheye image rectification algorithm with line constrains in the undistorted perspective image plane. A novel Multi-Label Energy Optimization (MLEO) method is adopted to merge short circular arcs sharing the same or the approximately same circular parameters and select long circular arcs for camera rectification. Further we propose an efficient method to estimate intrinsic parameters of the fisheye camera by automatically selecting three properly arranged long circular arcs from previously obtained circular arcs in the calibration procedure. Experimental results on a number of real images and simulated data show that the proposed method can achieve good results and outperforms the existing approaches and the commercial software in most cases.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Mi and Yao, Jian and Xia, Menghan and Li, Kai and Zhang, Yi and Liu, Yaping},title = {Line-Based Multi-Label Energy Optimization for Fisheye Image Rectification and Calibration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


David Perra, Rohit Kumar Gupta, Jan-Michael Frahm

Abstract: We present a novel, continuous, locally optimal calibration scheme for use with head-worn devices.  Current calibration schemes solve for a globally optimal model of the eye-device transformation by performing calibration on a per-user or once-per-use basis.  However, these calibration schemes are impractical for real-world applications because they do not account for changes in calibration during the time of use.  Our calibration scheme allows a head-worn device to calculate a locally optimal eye-device transformation on demand by computing an optimal model from a local window of previous frames. By leveraging naturally occurring interest regions within the user's environment, our system can calibrate itself without the user's active participation.  Experimental results demonstrate that our proposed calibration scheme outperforms the existing state of the art systems while being significantly less restrictive to the user and the environment.

@InProceedings{Perra_2015_CVPR,author = {Perra, David and Kumar Gupta, Rohit and Frahm, Jan-Michael},title = {Adaptive Eye-Camera Calibration for Head-Worn Devices},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Daniyar Turmukhambetov, Neill D.F. Campbell, Simon J.D. Prince, Jan Kautz

Abstract: Subspace models have been very successful at modeling the appearance of structured image datasets when the visual objects have been aligned in the images (e.g., faces). Even with extensions that allow for global transformations or dense warps of the image, the set of visual objects whose appearance may be modeled by such methods is limited. They are unable to account for visual objects where occlusion leads to changing visibility of different object parts (without a strict layered structure) and where a one-to-one mapping between parts is not preserved. For example bunches of bananas contain different numbers of bananas but each individual banana shares an appearance subspace.   In this work we remove the image space alignment limitations of existing subspace models by conditioning the models on a shape dependent context that allows for the complex, non-linear structure of the appearance of the visual object to be captured and shared. This allows us to exploit the advantages of subspace appearance models with non-rigid, deformable objects whilst also dealing with complex occlusions and varying numbers of parts. We demonstrate the effectiveness of our new model with examples of structured inpainting and appearance transfer.

@InProceedings{Turmukhambetov_2015_CVPR,author = {Turmukhambetov, Daniyar and Campbell, Neill D.F. and Prince, Simon J.D. and Kautz, Jan},title = {Modeling Object Appearance Using Context-Conditioned Component Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this paper, we therefore propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The proposed displets encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel based CRF framework and demonstrate its benefits on the KITTI stereo evaluation. At time of submission, our approach ranks first across all KITTI stereo leaderboards.

@InProceedings{Guney_2015_CVPR,author = {Guney, Fatma and Geiger, Andreas},title = {Displets: Resolving Stereo Ambiguities Using Object Knowledge},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Time-to-Contact From Image Intensity

Yukitoshi Watanabe, Fumihiko Sakaue, Jun Sato

Abstract: It is known that time-to-contact toward objects can be estimated just from changes in the object size in camera images, and we do not need any additional information, such as camera parameters and motions.  However, the existing methods for measuring the time-to-contact are based on geometric image features, such as corners and edge lines, and thus they cannot be used when there are no geometric features in images. In this paper, we propose a new method for computing the time-to-contact from photometric information in images. When a light source moves in the 3D scene, an observed intensity changes according to the motion of the light source.  In this paper, we analyze the change in photometric information in images, and show that the time-to-contact can be estimated just from the changes in intensity in images. Our method does not need any additional information, such as radiance of light source, reflectance of object and orientation of object surface. The proposed method can be used in various applications, such as vehicle driver assistance.

@InProceedings{Watanabe_2015_CVPR,author = {Watanabe, Yukitoshi and Sakaue, Fumihiko and Sato, Jun},title = {Time-to-Contact From Image Intensity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang

Abstract: Learning semantic attributes for person re-identification and  description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their non-scalable need for extensive domain (camera) specific annotation.  In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets -- either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.

@InProceedings{Shi_2015_CVPR,author = {Shi, Zhiyuan and Hospedales, Timothy M. and Xiang, Tao},title = {Transferring a Semantic Representation for Person Re-Identification and Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhengyang Wu, Fuxin Li, Rahul Sukthankar, James M. Rehg

Abstract: We propose a robust algorithm to generate video segment proposals. The proposals generated by our method can start from any frame in the video and are robust to complete occlusions. Our method does not assume specific motion models and even has a limited capability to generalize across videos. We build on our previous least squares tracking framework, where image segment proposals are generated and tracked using learned appearance models. The innovation in our new method lies in the use of two efficient moves, the merge move and free addition, to efficiently start segments from any frame and track them through complete occlusions, without much additional computation. Segment size interpolation is used for effectively detecting occlusions.  We propose a new metric for evaluating video segment proposals on the challenging VSB-100 benchmark and present state-of-the-art results. Preliminary results are also shown for the potential use of our framework to track segments across different videos.

@InProceedings{Wu_2015_CVPR,author = {Wu, Zhengyang and Li, Fuxin and Sukthankar, Rahul and Rehg, James M.},title = {Robust Video Segment Proposals With Painless Occlusion Handling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Donghoon Lee, Hyunsin Park, Chang D. Yoo

Abstract: In this paper, we propose a face alignment method that uses cascade Gaussian process regression trees (cGPRT) constructed by combining Gaussian process regression trees (GPRT) in a cascade stage-wise manner. Here, GPRT is a Gaussian process with a kernel defined by a set of trees. The kernel measures the similarity between two inputs as the number of trees where the two inputs fall in the same leaves. Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization. Features for GPRT are designed using shape-indexed difference of Gaussian (DoG) filter responses sampled from local retinal patterns to increase stability and to attain robustness against geometric variances. Compared with the previous CRT-based face alignment methods that have shown state-of-the-art performances, cGPRT using shape-indexed DoG features performed best on the HELEN and 300-W datasets which are the most challenging dataset today.

@InProceedings{Lee_2015_CVPR,author = {Lee, Donghoon and Park, Hyunsin and Yoo, Chang D.},title = {Face Alignment Using Cascade Gaussian Process Regression Trees},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Part-based models are one of the leading paradigms in visual recognition. In the absence of costly part annotations, associating and aligning different training instances of a part classifier and finding characteristic negatives is challenging and computationally demanding. To avoid this costly mining of training samples, we estimate separate generative models for negatives and positives and integrate them into a max-margin exemplar-based model. The generative model and a sparsity constraint on the correlation between spatially neighboring feature dimensions regularize the part filters during learning and improve their generalization to similar instances. To suppress inappropriate positive part samples, we project the classifier back into the image domain and penalize against deviations from the original exemplar image patch. The part filter is then optimized to i) discriminate against clutter, to ii) generalize to similar instances of the part, and iii) to yield a good reconstruction of the original image patch. Moreover, we propose an approximation for estimating the geometric margin so that learning large numbers of parts becomes feasible. Experiments show improved part localization, object recognition, and part-based reconstruction performance compared to popular exemplar-based approaches on PASCAL VOC

@InProceedings{Rubio_2015_CVPR,author = {Rubio, Jose C. and Ommer, Bjorn},title = {Regularizing Max-Margin Exemplars by Reconstruction and Generative Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Gunay Dogan, Javier Bernal, Charles R. Hagwood

Abstract: Effective computational tools for shape analysis are needed in many areas of science and engineering. We address this and propose a new fast iterative algorithm to compute the elastic geodesic distance between shapes of closed planar curves. The original algorithm for this has cubic time complexity with respect to the number of nodes per curve. Hence it is not suitable for large shape data sets. We aim for large-scale shape analysis and thus propose an iterative algorithm based on the original one but with quadratic time complexity. In practice, we observe subquadratic, almost linear running times, and that our algorithm scales very well with large numbers of nodes. The key to our algorithm is the decoupling of the optimization for the starting point and rotation from that of the reparametrization, and the development of fast dynamic programming and iterative nonlinear constrained optimization algorithms that work in tandem to compute optimal reparametrizations fast.

@InProceedings{Dogan_2015_CVPR,author = {Dogan, Gunay and Bernal, Javier and Hagwood, Charles R.},title = {A Fast Algorithm for Elastic Shape Distances Between Closed Planar Curves},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In-vehicle black box camera becomes an popular equipment in many countries for security monitoring and event capturing. The readability of video contents is the most important capability, which is, however, often degraded due to the reflection on the windscreen. In this paper, we propose a novel method to remove the reflection on the windscreen in the in-vehicle black box videos. The main idea is to exploit spatio-temporal coherence of the reflection which shows that a vehicle moves forward while the reflection layer of internal object remains static. The average image prior is introduced by imposing a heavy-tail distribution with higher peak. The two layered scene is the base of the separation model. In order to remove the reflection, a cost non-convex function is developed based on this property and optimized. Experimental results demonstrate that the proposed approach successfully separates the layers in real black box videos.

@InProceedings{Simon_2015_CVPR,author = {Simon, Christian and Kyu Park, In},title = {Reflection Removal for In-Vehicle Black Box Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We propose a new vector encoding scheme (tree quantization) that obtains lossy compact codes for high-dimensional vectors via tree-based dynamic programming. Similarly to several previous schemes such as product quantization, these codes correspond to codeword numbers within multiple codebooks. We propose an integer programming-based optimization that jointly recovers the coding tree structure and the codebooks by minimizing the compression error on a training dataset. In the experiments with diverse visual descriptors (SIFT, neural codes, Fisher vectors), tree quantization is shown to combine fast encoding and state-of-the-art accuracy in terms of the compression error, the retrieval performance, and the image classification error.

@InProceedings{Babenko_2015_CVPR,author = {Babenko, Artem and Lempitsky, Victor},title = {Tree Quantization for Large-Scale Similarity Search and Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Bing Shuai, Gang Wang, Zhen Zuo, Bing Wang, Lifan Zhao

Abstract: We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishable from local context, we alleviate such ambiguity by putting a global scene constraint. We estimate the global potential in a non-parametric framework. Furthermore, a large margin based CNN metric learning method is proposed for better global potential estimation. The final pixel class prediction is performed by integrating local and global beliefs. Even without any post-processing, we achieve state-of-the-art on SiftFlow and competitive results on Stanford Background benchmark.

@InProceedings{Shuai_2015_CVPR,author = {Shuai, Bing and Wang, Gang and Zuo, Zhen and Wang, Bing and Zhao, Lifan},title = {Integrating Parametric and Non-Parametric Models For Scene Labeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yu-Wei Chao, Zhan Wang, Rada Mihalcea, Jia Deng

Abstract: Affordances are fundamental attributes of objects. Affordances reveal the functionalities of objects and the possible actions that can be performed on them. Understanding affordances is crucial for recognizing human activities in visual data and for robots to interact with the world.  In this paper we introduce the new problem of mining the knowledge of semantic affordance: given an object, determining whether an action can be performed on it. This is equivalent to connecting verb nodes and noun nodes in WordNet, or filling an affordance matrix encoding the plausibility of each action-object pair. We introduce a new benchmark with crowdsourced ground truth affordances on 20 PASCAL VOC object classes and 957 action classes.  We explore a number of approaches including text mining, visual mining, and collaborative filtering. Our analyses yield a number of significant insights that reveal the most effective ways of collecting knowledge of semantic affordances.

@InProceedings{Chao_2015_CVPR,author = {Chao, Yu-Wei and Wang, Zhan and Mihalcea, Rada and Deng, Jia},title = {Mining Semantic Affordances of Visual Object Categories},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Brian Taylor, Vasiliy Karasev, Stefano Soatto

Abstract: Occlusion relations inform the partition of the image domain into ``objects'' but are difficult to determine from a single image or short-baseline video. We show how long-term occlusion relations can be robustly inferred from video, and used within a convex optimization framework to segment the image domain into regions. We highlight the challenges in determining these occluder/occluded relations and ensuring regions remain temporally consistent, propose strategies to overcome them, and introduce an efficient numerical scheme to perform the partition directly on the pixel grid, without the need for superpixelization or other preprocessing steps.

@InProceedings{Taylor_2015_CVPR,author = {Taylor, Brian and Karasev, Vasiliy and Soatto, Stefano},title = {Causal Video Object Segmentation From Persistence of Occlusions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: A generalized formulation of the multiple instance learning problem is considered. Under this formulation, both positive and negative bags are soft, in the sense that negative bags can also contain positive instances. This reflects a problem setting commonly found in practical applications, where labeling noise appears on both positive and negative training samples. A novel bag-level representation is introduced, using instances that are most  likely to be positive (denoted top instances), and its ability to separate soft bags, depending on their relative composition in terms of positive and negative instances, is studied. This study inspires a new large-margin algorithm for soft-bag classification, based on a latent support vector machine that efficiently explores the combinatorial space of bag compositions. Empirical evaluation on three datasets is shown to confirm the main findings of the theoretical analysis and the effectiveness of the  proposed soft-bag classifier.

@InProceedings{Li_2015_CVPR,author = {Li, Weixin and Vasconcelos, Nuno},title = {Multiple Instance Learning for Soft Bags via Top Instances},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We address the problem of integrating object reasoning with supervoxel labeling in multiclass semantic video segmentation. To this end, we first propose an object-augmented dense CRF in spatio-temporal domain, which captures long-range dependency between supervoxels, and imposes consistency between object and supervoxel labels. We develop an efficient mean field inference algorithm to jointly infer the supervoxel labels, object activations and their occlusion relations for a moderate number of object proposals.    To scale up our method, we adopt an active inference strategy to improve the efficiency, which adaptively selects object subgraphs in the object-augmented dense CRF. We formulate the problem as a Markov Decision Process, which learns an approximate optimal policy based on a reward of accuracy improvement and a set of well-designed model and input features. We evaluate our method on three publicly available multiclass video semantic segmentation datasets and demonstrate superior efficiency and accuracy.

@InProceedings{Liu_2015_CVPR,author = {Liu, Buyu and He, Xuming},title = {Multiclass Semantic Video Segmentation With Object-Level Active Inference},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Tal Hassner, Shai Harel, Eran Paz, Roee Enbar

Abstract: "Frontalization" is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems. This, by transforming the challenging problem of recognizing faces viewed from unconstrained viewpoints to the easier problem of recognizing faces in constrained, forward facing poses. Previous frontalization methods did this by attempting to approximate 3D facial shapes for each query image. We observe that 3D face shape estimation from unconstrained photos may be a harder problem than frontalization and can potentially introduce facial misalignments. Instead, we explore the simpler approach of using a single, unmodified, 3D surface as an approximation to the shape of all input faces. We show that this leads to a straightforward, efficient and easy to implement method for frontalization. More importantly, it produces aesthetic new frontal views and is surprisingly effective when used for face recognition and gender estimation.

@InProceedings{Hassner_2015_CVPR,author = {Hassner, Tal and Harel, Shai and Paz, Eran and Enbar, Roee},title = {Effective Face Frontalization in Unconstrained Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features and deep-learned features. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMDB51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features and deep-learned features. Our method also achieves superior performance to the state of the art on these datasets.

@InProceedings{Wang_2015_CVPR,author = {Wang, Limin and Qiao, Yu and Tang, Xiaoou},title = {Action Recognition With Trajectory-Pooled Deep-Convolutional Descriptors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We consider the problem of localizing unseen objects in weakly labeled image collections. Given a set of images annotated at the image level, our goal is to localize the object in each image. The novelty of our proposed work is in addition to building object appearance model from the weakly labeled data, we also make use of existing detectors for some other object classes (which we call ``familiar objects''). We propose a method for transferring the appearance models of the familiar objects to the unseen object. Our experimental results on both image and video datasets demonstrate the effectiveness of our approach.

@InProceedings{Rochan_2015_CVPR,author = {Rochan, Mrigank and Wang, Yang},title = {Weakly Supervised Localization of Novel Objects Using Appearance Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Gregory Rogez, James S. Supancic III, Deva Ramanan

Abstract: We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider pose estimation during everyday interactions with objects. Past work shows that strong pose+viewpoint priors and depth-based features are crucial for robust performance. In egocentric views, hands and arms are observable within a well defined volume in front of the camera. We call this volume an egocentric workspace. A notable property is that hand appearance correlates with workspace location. To exploit this correlation, we classify arm+hand configurations in a global egocentric coordinate frame, rather than a local scanning window. This greatly simplify the architecture and improves performance. We propose an efficient pipeline which 1) generates synthetic  workspace exemplars for training using a virtual chest-mounted camera whose intrinsic parameters match our physical camera, 2) computes perspective-aware depth features on this entire volume and 3) recognizes discrete arm+hand pose classes through a sparse multi-class SVM. We achieve state-of-the-art hand pose recognition performance from egocentric RGB-D images in real-time.

@InProceedings{Rogez_2015_CVPR,author = {Rogez, Gregory and Supancic, III, James S. and Ramanan, Deva},title = {First-Person Pose Recognition Using Egocentric Workspaces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Changpeng Ti, Ruigang Yang, James Davis, Zhigeng Pan

Abstract: We present a novel system which incorporates photometric stereo with the Time-of-Flight depth sensor. Adding to the classic ToF, the system utilizes multiple point light sources that enable the capturing of a normal field whilst taking depth images. Two calibration methods are proposed to determine the light sources' positions given the ToF sensor's relatively low resolution. An iterative refinement algorithm is formulated to account for the extra phase delays caused by the positions of the light sources. We find in experiments that the system is comparable to the classic ToF in depth accuracy, and it is able to recover finer details that are lost due to the noise level of the ToF sensor.

@InProceedings{Ti_2015_CVPR,author = {Ti, Changpeng and Yang, Ruigang and Davis, James and Pan, Zhigeng},title = {Simultaneous Time-of-Flight Sensing and Photometric Stereo With a Single ToF Sensor},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Christoph Kading, Alexander Freytag, Erik Rodner, Paul Bodesheim, Joachim Denzler

Abstract: Current visual recognition algorithms are "hungry" for data but massive annotation is extremely costly. Therefore, active learning algorithms are required that reduce labeling efforts to a minimum by selecting examples that are most valuable for labeling. In active learning, all categories occurring in collected data are usually assumed to be known in advance and experts should be able to label every requested instance. But do these assumptions really hold in practice? Could you name all categories in every image?  Existing algorithms completely ignore the fact that there are certain examples where an oracle can not provide an answer or which even do not belong to the current problem domain. Ideally, active learning techniques should be able to discover new classes and at the same time cope with queries an expert is not able or willing to label.   To meet these observations, we present a variant of the expected model output change principle for active learning and discovery in the presence of unnameable instances. Our experiments show that in these realistic scenarios, our approach substantially outperforms previous active learning methods, which are often not even able to improve with respect to the baseline of random query selection.

@InProceedings{Kading_2015_CVPR,author = {Kading, Christoph and Freytag, Alexander and Rodner, Erik and Bodesheim, Paul and Denzler, Joachim},title = {Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we  opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art  on several problems and benchmark datasets.

@InProceedings{Zagoruyko_2015_CVPR,author = {Zagoruyko, Sergey and Komodakis, Nikos},title = {Learning to Compare Image Patches via Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chenxia Wu, Jiemi Zhang, Silvio Savarese, Ashutosh Saxena

Abstract: We focus on modeling human activities comprising multiple actions in a completely unsupervised setting. Our model learns the high-level action co-occurrence and temporal relations between the actions in the activity video. We consider the video as a sequence of short-term action clips, called action-words, and an activity is about a set of action-topics indicating which actions are present in the video. Then we propose a new probabilistic model relating the action-words and the action-topics. It allows us to model long-range action relations that commonly exist in the complex activity, which is challenging to capture in the previous works. We apply our model to unsupervised action segmentation and recognition, and also to a novel application that detects forgotten actions, which we call action patching.  For evaluation, we also contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacted with different objects. The extensive experiments show the effectiveness of our model.

@InProceedings{Wu_2015_CVPR,author = {Wu, Chenxia and Zhang, Jiemi and Savarese, Silvio and Saxena, Ashutosh},title = {Watch-n-Patch: Unsupervised Understanding of Actions and Relations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Lianli Gao, Jingkuan Song, Feiping Nie, Yan Yan, Nicu Sebe, Heng Tao Shen

Abstract: In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available.     This is often done by adding a geometrically based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimal graph (OGL) from multi-cues (i.e., partial tags and multiple features) which can more accurately embed the relationships among the data points. We further extend our model to address out-of-sample and noisy label issues.    Extensive experiments on four public datasets show the consistent superiority of OGL over state-of-the-art methods by up to 12% in terms of mean average precision.

@InProceedings{Gao_2015_CVPR,author = {Gao, Lianli and Song, Jingkuan and Nie, Feiping and Yan, Yan and Sebe, Nicu and Tao Shen, Heng},title = {Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Gedas Bertasius, Jianbo Shi, Lorenzo Torresani

Abstract: Contour detection has been a fundamental component in many image segmentation and object detection systems. Most previous work utilizes low-level features such as texture or saliency to detect contours and then use them as cues for a higher-level task such as object detection. However, we claim that recognizing objects and predicting contours are two mutually related tasks. Contrary to traditional approaches, we show that we can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, we exploit object-related features as high-level cues for contour detection.   We achieve this goal by means of a multi-scale deep network that consists of five convolutional layers and a bifurcated fully-connected sub-network. The section from the input layer to the fifth convolutional layer is fixed and directly lifted from a pre-trained network optimized over a large-scale object classification task. This section of the network is applied to four different scales of the image input. These four parallel and identical streams are then attached to a bifurcated sub-network consisting of two independently-trained branches. One branch learns to predict the contour likelihood (with a classification objective) whereas the other branch is trained to learn the fraction of human labelers agreeing about the contour presence at a given point (with a regression criterion).   We show that without any feature engineering our multi-scale deep learning approach achieves state-of-the-art results in contour detection.

@InProceedings{Bertasius_2015_CVPR,author = {Bertasius, Gedas and Shi, Jianbo and Torresani, Lorenzo},title = {DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}