Yunchao Gong, Marcin Pawlowski, Fei Yang, Louis Brandy, Lubomir Bourdev, Rob Fergus

Abstract: This paper addresses the problem of clustering a very large number of photos (i.e. hundreds of millions a day) in a stream into millions of clusters. This is particularly important as the popularity of photo sharing websites, such as Facebook, Google, and Instagram. Given large number of photos available online, how to efficiently organize them is an open problem.   To address this problem, we propose to cluster the binary hash codes of a large number of photos into binary cluster centers. We present a fast binary k-means algorithm that works directly on the similarity-preserving hashes of images and clusters them into binary centers on which we can build hash indexes to speedup computation. The proposed method is capable of clustering millions of photos on a single machine in a few minutes. We show that this approach is usually several magnitude faster than standard k-means and produces comparable clustering accuracy.  In addition, we propose an online clustering method based on binary k-means that is capable of clustering large photo stream on a single machine, and show applications to spam detection and trending photo discovery.

@InProceedings{Gong_2015_CVPR,author = {Gong, Yunchao and Pawlowski, Marcin and Yang, Fei and Brandy, Louis and Bourdev, Lubomir and Fergus, Rob},title = {Web Scale Photo Hash Clustering on A Single Machine},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Propagated Image Filtering

Jen-Hao Rick Chang, Yu-Chiang Frank Wang

Abstract: In this paper, we propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions. In particular, our filter does not to utilize explicit spatial kernel functions as bilateral and guided filters do. We will show that our propagation filter can be viewed as a robust estimator, which minimizes the expected difference between the filtered and desirable image outputs. We will also relate propagation filtering to belief propagation, and suggest techniques if further speedup of the filtering process is necessary. In our experiments, we apply our propagation filter to a variety of applications such as image denoising, smoothing, fusion, and high-dynamic-range (HDR) compression. We will show that improved performance over existing image filters can be achieved.

@InProceedings{Chang_2015_CVPR,author = {Rick Chang, Jen-Hao and Frank Wang, Yu-Chiang},title = {Propagated Image Filtering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Going Deeper With Convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation of this architecture, GoogLeNet, a 22 layers deep network, was used to assess its  quality in the context of object detection and classification.

@InProceedings{Szegedy_2015_CVPR,author = {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},title = {Going Deeper With Convolutions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, Leonid Sigal

Abstract: Over the last several years it has been shown that image-based object detectors are sensitive to  the training data and often fail to generalize to examples that fall outside the original training  sample domain (e.g., videos).  A number of domain adaptation (DA) techniques have been proposed to address this problem.  DA approaches are designed to adapt a fixed complexity model to the new (e.g., video) domain. We posit that unlabeled data should not only allow adaptation, but also improve (or at least maintain) performance on  the original and  other domains by dynamically adjusting model complexity and parameters.  We call this notion domain expansion.  To this end, we develop a new scalable and accurate incremental object detection algorithm,  based on several extensions of large-margin embedding (LME). Our detection model consists of an embedding space and multiple class prototypes in that embedding space,  that represent object classes; distance to those prototypes allows us to reason about multi-class detection. By incrementally detecting object instances in video and adding confident detections into the model, we are able to dynamically adjust the complexity of the detector over time  by instantiating new prototypes to span all domains  the model has seen. We test performance of our approach by expanding an object detector trained on ImageNet to detect objects  in ego-centric videos of Activity Daily Living (ADL) dataset and challenging YouTube Objects (YTO) dataset.

@InProceedings{Kuznetsova_2015_CVPR,author = {Kuznetsova, Alina and Ju Hwang, Sung and Rosenhahn, Bodo and Sigal, Leonid},title = {Expanding Object Detector's Horizon: Incremental Learning Framework for Object Detection in Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Supervised Discrete Hashing

Fumin Shen, Chunhua Shen, Wei Liu, Heng Tao Shen

Abstract: Recently, learning based hashing techniques have attracted broad research interests due to the resulting efficient storage and retrieval of images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the needed hash codes, which typically makes hash optimizations very challenging (NP-hard in general). In this work, we propose a new supervised hashing framework, where the learning objective for hashing is to make the optimal binary hash codes for classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by using a regularization algorithm. One of the key steps in the algorithm is to solve the regularization sub-problem associated with the NP-hard binary optimization. We show that with cyclic coordinate descent, the sub-problem admits an analytical solution. As such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, which enables to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets, and demonstrate that SDH outperforms the state-of-the-art hashing methods in large-scale image retrieval.

@InProceedings{Shen_2015_CVPR,author = {Shen, Fumin and Shen, Chunhua and Liu, Wei and Tao Shen, Heng},title = {Supervised Discrete Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Rahaf Aljundi, Remi Emonet, Damien Muselet, Marc Sebban

Abstract: Domain adaptation (DA) has gained a lot of success in the recent years in computer vision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, we introduce a novel unsupervised DA approach based on both subspace alignment and selection of landmarks similarly distributed between the two domains. Those landmarks are selected so as to reduce the discrepancy between the domains and then are used to non linearly project the data in the same space where an efficient subspace alignment (in closed-form) is performed. We carry out a large experimental comparison in visual domain adaptation showing that our new method outperforms the most recent unsupervised DA approaches.

@InProceedings{Aljundi_2015_CVPR,author = {Aljundi, Rahaf and Emonet, Remi and Muselet, Damien and Sebban, Marc},title = {Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mihir Jain, Jan C. van Gemert, Cees G. M. Snoek

Abstract: This paper contributes to automatic classification and localization of human actions in video. Whereas motion is the key ingredient in modern approaches, we assess the benefits of having objects in the video representation. Rather than considering a handful of carefully selected and localized objects, we conduct an empirical study on the benefit of encoding 15,000 object categories for action using 6 datasets totaling more than 200 hours of video and covering 180 action classes. Our key contributions are i) the first in-depth study of encoding objects for actions, ii) we show that objects matter for actions, and are often semantically relevant as well. iii) We establish that actions have object preferences. Rather than using all objects, selection is advantageous for action recognition. iv) We reveal that object-action relations are generic, which allows to transferring these relationships from the one domain to the other. And, v) objects, when combined with motion, improve the state-of-the-art for both action classification and localization.

@InProceedings{Jain_2015_CVPR,author = {Jain, Mihir and van Gemert, Jan C. and Snoek, Cees G. M.},title = {What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wei-Sheng Lai, Jian-Jiun Ding, Yen-Yu Lin, Yung-Yu Chuang

Abstract: This paper proposes a single-image blur kernel estimation algorithm that utilizes the normalized color-line prior to restore sharp edges without altering edge structures or enhancing noise. The proposed prior is derived from the color-line model, which has been successfully applied to non-blind deconvolution and many computer vision problems. In this paper, we show that the original color-line prior is not effective for blur kernel estimation and propose a normalized color-line prior which can better enhance edge contrasts. By optimizing the proposed prior, our method gradually enhances the sharpness of the intermediate patches without using heuristic filters or external patch priors. The intermediate patches can then guide the estimation of the blur kernel. A comprehensive evaluation on a large image deblurring dataset shows that our algorithm achieves the state-of-the-art results.

@InProceedings{Lai_2015_CVPR,author = {Lai, Wei-Sheng and Ding, Jian-Jiun and Lin, Yen-Yu and Chuang, Yung-Yu},title = {Blur Kernel Estimation Using Normalized Color-Line Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Simone Frintrop, Thomas Werner, German Martin Garcia

Abstract: In this paper, we show that the seminal, biologically-inspired   saliency model by Itti et al. is still competitive with current state-of-the-art methods for salient object   segmentation if some important adaptions are made.  We show which   changes are necessary to achieve high performance, with special   emphasis on the scale-space: we introduce a twin pyramid for   computing Difference-of-Gaussians, which enables a flexible   center-surround ratio.  The resulting system, called VOCUS2,   is elegant and coherent in structure, fast, and computes saliency at   the pixel level.  It is not only suitable for images with few objects, but also for   complex scenes as captured by mobile devices.  Furthermore, we   integrate the saliency system into an object proposal generation   framework to obtain segment-based saliency maps and boost the   results for salient object segmentation. We show that our system   achieves state-of-the-art performance on a large collection of   benchmark data.

@InProceedings{Frintrop_2015_CVPR,author = {Frintrop, Simone and Werner, Thomas and Martin Garcia, German},title = {Traditional Saliency Reloaded: A Good Old Model in New Shape},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang

Abstract: Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics. However, the depth images obtained from TOF cameras contain scene dependent errors due to multipath interference (MPI). Specifically, MPI occurs when multiple optical reflections return to a single spatial location on the imaging sensor. Many prior approaches to rectifying MPI rely on sparsity in optical reflections, which is an extreme simplification. In this paper, we correct MPI by combining the standard measurements from a TOF camera with information from direct and global light transport. We report results on both simulated experiments and physical experiments (using the Kinect sensor). Our results, evaluated against ground truth, demonstrate a quantitative improvement in depth accuracy.

@InProceedings{Naik_2015_CVPR,author = {Naik, Nikhil and Kadambi, Achuta and Rhemann, Christoph and Izadi, Shahram and Raskar, Ramesh and Bing Kang, Sing},title = {A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We address the elusive goal of estimating optical flow both accurately and efficiently by adopting a sparse-to-dense approach. Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields. We learn the principal components of natural flow fields using flow computed from four Hollywood movies. Optical flow fields are then compactly approximated as a weighted sum of the basis flow fields. Our new PCA-Flow algorithm robustly estimates these weights from sparse feature matches. The method runs in under 200ms/frame on the MPI-Sintel dataset using a single CPU and is more accurate and significantly faster than popular methods such as LDOF and Classic+NL. For some applications, however, the results are too smooth. Consequently, we develop a novel sparse layered flow method in which each layer is represented by PCA-Flow. Unlike existing layered methods, estimation is fast because it uses only sparse matches. We combine information from different layers into a dense flow field using an image-aware MRF. The resulting PCA-Layers method runs in 3.2s/frame, is significantly more accurate than PCA-Flow, and achieves state-of-the-art performance in occluded regions on MPI-Sintel.

@InProceedings{Wulff_2015_CVPR,author = {Wulff, Jonas and Black, Michael J.},title = {Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Carlo Ciliberto, Lorenzo Rosasco, Silvia Villa

Abstract: Multi-task learning is a natural approach for computer vision applications that require the simultaneous solution of several distinct but related problems, e.g. object detection, classification, tracking of multiple agents, or denoising, to name a few. The key idea is that exploring task relatedness (structure) can lead to better performances.  In this paper, we propose and study a novel sparse, non-parametric approach  exploiting the theory of Reproducing Kernel Hilbert Spaces for vector-valued functions. We develop a suitable regularization framework which can be formulated as a convex optimization problem,   and  is provably solvable using an alternating minimization approach. Empirical tests   show that the proposed method compares favorably to state of the art techniques and further allows to recover interpretable structures, a  problem of interest in its own right.

@InProceedings{Ciliberto_2015_CVPR,author = {Ciliberto, Carlo and Rosasco, Lorenzo and Villa, Silvia},title = {Learning Multiple Visual Tasks While Discovering Their Structure},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We propose a new approach to associate supervised learning-based confidence prediction with the stereo matching problem. First of all, we analyze the characteristics of various confidence measures in the regression forest framework to select effective confidence measures using training data. We then train regression forests again to predict the correctness (confidence) of a match by using selected confidence measures. In addition, we present a confidence-based matching cost modulation scheme based on the predicted correctness for improving the robustness and accuracy of various stereo matching algorithms. We apply the proposed scheme to the semi-global matching algorithm to make it robust under unexpected difficulties that can occur in outdoor environments. We verify the proposed confidence measure selection and cost modulation methods through extensive experimentation with various aspects using KITTI and challenging outdoor datasets.

@InProceedings{Park_2015_CVPR,author = {Park, Min-Gyu and Yoon, Kuk-Jin},title = {Leveraging Stereo Matching With Learning-Based Confidence Measures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Patrick Snape, Yannis Panagakis, Stefanos Zafeiriou

Abstract: In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images. We combine existing techniques for uncalibrated photometric stereo and low rank matrix decompositions in order to robustly recover a combined model of shape and identity. We build this basis without aid from a 3D model and show how it can be combined with recent efficient sparse facial feature localisation techniques to recover dense 3D facial shape. Unlike previous works in the area, our method is very efficient and is an order of magnitude faster to train, taking only a few minutes to build a model with over 2000 images. Furthermore, it can be used for real-time recovery of facial shape.

@InProceedings{Snape_2015_CVPR,author = {Snape, Patrick and Panagakis, Yannis and Zafeiriou, Stefanos},title = {Automatic Construction Of Robust Spherical Harmonic Subspaces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper, we introduce Cellular Automata--a dynamic evolution model to intuitively detect the salient object. First, we construct a background-based map using color and space contrast with the clustered boundary seeds. Then, a novel propagation mechanism dependent on Cellular Automata is proposed to exploit the intrinsic relevance of similar regions through interactions with neighbors. Impact factor matrix and coherence matrix are constructed to balance the influential power towards each cell's next state. The saliency values of all cells will be renovated simultaneously according to the proposed updating rule. It's surprising to find out that parallel evolution can improve all the existing methods to a similar level regardless of their original results. Finally, we present an integration algorithm in the Bayesian framework to take advantage of multiple saliency maps. Extensive experiments on six public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods.

@InProceedings{Qin_2015_CVPR,author = {Qin, Yao and Lu, Huchuan and Xu, Yiqun and Wang, He},title = {Saliency Detection via Cellular Automata},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper addresses the problem of uncalibrated photometric stereo with isotropic reflectances. Existing methods face difficulty in solving for the elevation angles of surface normals when the light sources only cover the visible hemisphere. Here, we introduce the notion of "constrained half-vector symmetry" for general isotropic BRDFs and show its capability of elevation angle recovery. This sort of symmetry can be observed in a 1D BRDF slice from a subset of surface normals with the same azimuth angle, and we use it to devise an efficient modeling and solution method to constrain and recover the elevation angles of surface normals accurately. To enable our method to work in an uncalibrated manner, we further solve for light sources in the case of general isotropic BRDFs. By combining this method with the existing ones for azimuth angle estimation, we can get state-of-the-art results for uncalibrated photometric stereo with general isotropic reflectances.

@InProceedings{Lu_2015_CVPR,author = {Lu, Feng and Sato, Imari and Sato, Yoichi},title = {Uncalibrated Photometric Stereo Based on Elevation Angle Recovery From BRDF Symmetry of Isotropic Materials},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


HyeokHyen Kwon, Yu-Wing Tai, Stephen Lin

Abstract: Depth maps captured by consumer-level depth cameras such as Kinect are usually degraded by noise, missing values, and quantization. In this paper, we present a data-driven approach for refining degraded RAW depth maps that are coupled with an RGB image. The key idea of our approach is to take advantage of a training set of high-quality depth data and transfer its information to the RAW depth map through multi-scale dictionary learning. Utilizing a sparse representation, our method learns a dictionary of geometric primitives which captures the correlation between high-quality mesh data, RAW depth maps and RGB images. The dictionary is learned and applied in a manner that accounts for various practical issues that arise in dictionary-based depth refinement. Compared to previous approaches that only utilize the correlation between RAW depth maps and RGB images, our method produces improved depth maps without over-smoothing. Since our approach is data driven, the refinement can be targeted to a specific class of objects by employing a corresponding training set. In our experiments, we show that this leads to additional improvements in recovering depth maps of human faces.

@InProceedings{Kwon_2015_CVPR,author = {Kwon, HyeokHyen and Tai, Yu-Wing and Lin, Stephen},title = {Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen

Abstract: In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose a novel method to learn the Projection Metric directly on Grassmann manifold rather than in Hilbert space. From the perspective of manifold learning, our method can be regarded as performing a geometry-aware dimensionality reduction from the original Grassmann manifold to a lower-dimensional, more discriminative Grassmann manifold  where more favorable classification can be achieved. Experiments on several real-world video face datasets demonstrate that the proposed method yields competitive performance compared with the state-of-the-art algorithms.

@InProceedings{Huang_2015_CVPR,author = {Huang, Zhiwu and Wang, Ruiping and Shan, Shiguang and Chen, Xilin},title = {Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Structural Sparse Tracking

Tianzhu Zhang, Si Liu, Changsheng Xu, Shuicheng Yan, Bernard Ghanem, Narendra Ahuja, Ming-Hsuan Yang

Abstract: Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Tianzhu and Liu, Si and Xu, Changsheng and Yan, Shuicheng and Ghanem, Bernard and Ahuja, Narendra and Yang, Ming-Hsuan},title = {Structural Sparse Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ran Tao, Arnold W.M. Smeulders, Shih-Fu Chang

Abstract: This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos. Firstly, we evaluate state-of-the-art instance search methods on this problem. We observe that what works for buildings loses its generality on shoes. Secondly, we propose to use automatically learned category-specific attributes to address the large appearance variations present in generic instance search. On the problem of searching among instances from the same category as the query, the category-specific attributes outperform existing approaches by a large margin. On a shoe dataset containing 6624 shoe images recorded from all viewing angles, we improve the performance from 36.73 to 56.56 using category-specific attributes. Thirdly, we extend our methods to search objects without restricting to the specifically known category. We show the combination of category-level information and the category-specific attributes is superior to combining category-level information with low-level features such as Fisher vector.

@InProceedings{Tao_2015_CVPR,author = {Tao, Ran and Smeulders, Arnold W.M. and Chang, Shih-Fu},title = {Attributes and Categories for Generic Instance Search From One Example},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Christopher Zach, Adrian Penate-Sanchez, Minh-Tri Pham

Abstract: Joint object recognition and pose estimation solely from range images is an important task e.g. in robotics applications and in automated manufacturing environments. The lack of color information and limitations of current commodity depth sensors make this task a challenging computer vision problem, and a standard random sampling based approach is prohibitively time-consuming. We propose to address this difficult problem by generating promising inlier sets for pose estimation by early rejection of clear outliers with the help of local belief propagation (or dynamic programming). By exploiting data-parallelism our method is fast, and we also do not rely on a computationally expensive training phase. We demonstrate state-of-the art performance on a standard dataset and illustrate our approach on challenging real sequences.

@InProceedings{Zach_2015_CVPR,author = {Zach, Christopher and Penate-Sanchez, Adrian and Pham, Minh-Tri},title = {A Dynamic Programming Approach for Fast and Robust Object Pose Recognition From Range Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zhengzhong Lan, Ming Lin, Xuanchong Li, Alex G. Hauptmann, Bhiksha Raj

Abstract: Most state-of-the-art action feature extractors involve differential operators, which act as highpass filters and tend to attenuate low frequency action information. This attenuation introduces bias to the resulting features and generates ill-conditioned feature matrices. The Gaussian Pyramid has been used as a feature enhancing technique that encodes scale-invariant characteristics into the feature space in an attempt to deal with this attenuation. However, at the core of the Gaussian Pyramid is a convolutional smoothing operation, which makes it incapable of generating new features at coarse scales. In order to address this problem, we propose a novel feature enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks features extracted using a family of differential filters  parameterized with multiple time skips and encodes shift-invariance into the frequency space. MIFS compensates for information lost from using differential operators by recapturing information at coarse scales. This recaptured information allows us to match actions at different speeds and ranges of motion.  We prove that MIFS enhances the learnability of differential-based features exponentially. The resulting feature matrices from MIFS have much smaller conditional numbers and variances than those from conventional methods. Experimental results show significantly improved performance on challenging action recognition and event detection tasks. Specifically, our method exceeds the state-of-the-arts on Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup strategy for feature extraction with minimal or no accuracy cost.

@InProceedings{Lan_2015_CVPR,author = {Lan, Zhengzhong and Lin, Ming and Li, Xuanchong and Hauptmann, Alex G. and Raj, Bhiksha},title = {Beyond Gaussian Pyramid: Multi-Skip Feature Stacking for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Shaoxin Li, Junliang Xing, Zhiheng Niu, Shiguang Shan, Shuicheng Yan

Abstract: One key challenge of facial traits recognition is the large non-rigid appearance variations due to irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangling such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial traits recognition tasks, including identity, age and gender classification. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

@InProceedings{Li_2015_CVPR,author = {Li, Shaoxin and Xing, Junliang and Niu, Zhiheng and Shan, Shiguang and Yan, Shuicheng},title = {Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mostafa Abdelrahman, Aly Farag, David Swanson, Moumen T. El-Melegy

Abstract: This paper propose an approach for modeling textured 3D non-rigid models based on Weighted Heat Kernel Signature(W-HKS). As a first contribution, we show how to include photometric information as a weight over the shape manifold, we also propose a novel formulation for heat diffusion over weighted manifolds. As a second contribution we present a new discretization method for the proposed equation using finite element approximation. Finally, the weighted heat kernel signature is used as a shape descriptor. The proposed descriptor encodes both the photometric, and geometric information based on the solution of one equation. We also propose a new method to introduce the scale invariance for the weighted heat kernel signature. The performance is tested on two benchmark datasets. The results have indeed confirmed the high performance of the proposed approach on the textured shape retrieval problem, and show that the proposed method is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.

@InProceedings{Abdelrahman_2015_CVPR,author = {Abdelrahman, Mostafa and Farag, Aly and Swanson, David and El-Melegy, Moumen T.},title = {Heat Diffusion Over Weighted Manifolds: A New Descriptor for Textured 3D Non-Rigid Shapes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Dongping Li, Kaiming He, Jian Sun, Kun Zhou

Abstract: The manipulation of panoramic/wide-angle images is usually achieved via image warping. Though various techniques have been developed for preserving shapes and straight lines for warping, these are not sufficient for panoramic/wide-angle images. The image projections will turn the straight lines into curved "geodesic lines", and it is fundamentally impossible to keep all these lines straight. In this work, we propose a geodesic-preserving method for content-aware image warping. An energy term is introduced to preserve the geodesic appearance of the geodesic lines, and can be used with shape-preserving terms. Our method is demonstrated in various applications, including rectangling panoramas, resizing panoramic/wide-angle images, and wide-angle image manipulation. An extension to ellipse preservation for general images is also presented.

@InProceedings{Li_2015_CVPR,author = {Li, Dongping and He, Kaiming and Sun, Jian and Zhou, Kun},title = {A Geodesic-Preserving Method for Image Warping},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Benjamin Allain, Jean-Sebastien Franco, Edmond Boyer

Abstract: Recovering 3D shape motion using visual information is an important problem with many applications in computer vision and computer graphics, among other domains.  Most existing approaches rely on surface-based strategies, where surface models are fit to visual surface observations. While numerically plausible, this paradigm ignores the fact that the observed surfaces often delimit volumetric shapes, for which deformations are constrained by the volume inside the shape. Consequently, surface-based strategies can fail when the observations define several feasible surfaces, whereas volumetric considerations are more restrictive with respect to the admissible solutions. In this work, we investigate a novel volumetric shape parametrization to track shapes over temporal sequences. In constrast to Eulerian grid discretizations of the observation space, such as voxels, we consider general shape tesselations yielding more convenient cell decompositions, in particular the Centroidal Voronoi Tesselation. With this shape representation, we devise a tracking method that exploits volumetric information, both for the data term evaluating observation conformity, and for expressing deformation constraints that enforce prior assumptions on motion. Experiments on several datasets demonstrate similar or improved precisions over state-of-the-art methods, as well as improved robustness, a critical issue when tracking sequentially over time frames.

@InProceedings{Allain_2015_CVPR,author = {Allain, Benjamin and Franco, Jean-Sebastien and Boyer, Edmond},title = {An Efficient Volumetric Framework for Shape Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Spyridon Leonardos, Roberto Tron, Kostas Daniilidis

Abstract: The trifocal tensor, which describes the relation between projections of points and lines in three views, is a fundamental entity of geometric computer vision. In this work, we investigate a new parametrization of the trifocal tensor for calibrated cameras with non-colinear pinholes obtained from a quotient Riemannian manifold.  We incorporate this formulation into state-of-the art methods for optimization on manifolds, and show, through experiments in pose averaging, that it produces a meaningful way to measure distances between trifocal tensors.

@InProceedings{Leonardos_2015_CVPR,author = {Leonardos, Spyridon and Tron, Roberto and Daniilidis, Kostas},title = {A Metric Parametrization for Trifocal Tensors With Non-Colinear Pinholes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper presents effective combination models with certain combination features for human detection. In the past several years, many existing features/models have achieved impressive progress, but their performances are still limited by the biases rooted in their self-structures, that is, a particular kind of feature/model may work well for some types of human bodies, but not for all the types. To tackle this difficult problem, we combine certain complementary features/models together with effective organization/fusion methods. Specifically, the HOG features, color features and bar-shape features are combined together with a cell-based histogram structure to form the so-called HOG-III features. Moreover, the detections from different models are fused together with the new proposed weighted-NMS algorithm, which enhances the probable "true" activations as well as suppresses the overlapped detections. The experiments on PASCAL VOC datasets demonstrate that, both the HOG-III features and the weighted-NMS fusion algorithm are effective (obvious improvement for detection performance) and efficient (relatively less computation cost): When applied to human detection task with the Grammar model and Poselet model, they can boost the detection performance significantly; Also, when extended to detection of the whole VOC 20 object categories with the deformable part-based model and deepCNN-based model, they still show competitive improvements.

@InProceedings{Jiang_2015_CVPR,author = {Jiang, Yunsheng and Ma, Jinwen},title = {Combination Features and Models for Human Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee

Abstract: Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of error for detection. Building upon high-capacity CNN architectures, we address the localization problem by 1) using a search algorithm based on Bayesian optimization that sequentially proposes candidate regions for an object bounding box, and 2) training the CNN with a structured loss that explicitly penalizes the localization inaccuracy. In experiments, we demonstrated that each of the proposed methods improves the detection performance over the baseline method on PASCAL VOC 2007 and 2012 datasets. Furthermore, two methods are complementary and significantly outperform the previous state-of-the-art when combined.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Yuting and Sohn, Kihyuk and Villegas, Ruben and Pan, Gang and Lee, Honglak},title = {Improving Object Detection With Deep Convolutional Networks via Bayesian Optimization and Structured Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Marko Ristin, Juergen Gall, Matthieu Guillaumin, Luc Van Gool

Abstract: The number of digital images is growing extremely rapidly, and so is the need for their classification. But, as more images of pre-defined categories become available, they also become more diverse and cover   finer semantic differences. Ultimately, the categories themselves need to be divided into subcategories to account for that semantic refinement. Image classification in general has improved significantly over  the last few years, but it still requires a massive amount of manually annotated data. Subdividing categories into subcategories multiples the number of labels, aggravating the annotation problem. Hence, we    can expect the annotations to be refined only for a subset of the already labeled data, and exploit coarser labeled data to improve classification. In this work, we investigate how coarse category labels can   be used to improve the classification of subcategories. To this end, we adopt the framework of Random Forests and propose a regularized objective function that takes into account relations between categories   and subcategories. Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image            classification experiments.

@InProceedings{Ristin_2015_CVPR,author = {Ristin, Marko and Gall, Juergen and Guillaumin, Matthieu and Van Gool, Luc},title = {From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State of the art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to state of the art results in many applications, it is sub-optimal because it does not exploit the fact that the affinity and the segmentation depend on each other. In this paper, we propose a unified optimization framework for learning both the affinity and the segmentation. Our framework is based on expressing each data point as a structured sparse linear combination of all other data points, where the structure is induced by a norm that depends on the unknown segmentation. We show that both the segmentation and the structured sparse representation can be found via a combination of an alternating direction method of multipliers with spectral clustering. Experiments on a synthetic data, the Hopkins 155 motion segmentation database, and the Extended Yale B data set demonstrate the effectiveness of our approach.

@InProceedings{Li_2015_CVPR,author = {Li, Chun-Guang and Vidal, Rene},title = {Structured Sparse Subspace Clustering: A Unified Optimization Framework},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: In this paper, we present a computationally efficient and non-parametric method for robust regression on manifolds. We apply our algorithm to the problem of correcting mislabeled examples from image collections with ordered (e.g., real-valued, ordinal) labels. Compared to related methods for robust regression, our method achieves superior denoising accuracy on a variety of data sets, with label corruption levels as high as 80%. For a diverse set of widely-used, large-scale, publicly-available data sets, our approach results in image labels that more accurately describe the associated images.

@InProceedings{Wu_2015_CVPR,author = {Wu, Hui and Souvenir, Richard},title = {Robust Regression on Image Manifolds for Ordered Label Denoising},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Delving Into Egocentric Actions

Abstract: We address the challenging problem of recognizing the camera wearer's actions from videos captured by an egocentric camera. Egocentric videos encode a rich set of signals regarding the camera wearer, including head movement, hand pose and gaze information. We propose to utilize these mid-level egocentric cues for egocentric action recognition. We present a novel set of egocentric features and show how they can be combined with motion and object features. The result is a compact representation with superior performance. In addition, we provide the first systematic evaluation of motion, object and egocentric cues in egocentric action recognition. Our benchmark leads to several surprising findings. These findings uncover the best practices for egocentric actions, with a significant performance boost over all previous state-of-the-art methods on three publicly available datasets.

@InProceedings{Li_2015_CVPR,author = {Li, Yin and Ye, Zhefan and Rehg, James M.},title = {Delving Into Egocentric Actions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: The next wave of micro and nano devices will create a world with trillions of small networked cameras. This will lead to increased concerns about privacy and security. Most privacy preserving algorithms for computer vision are applied after image/video data has been captured. We propose to use privacy preserving optics that filter or block sensitive information directly from the incident light-field before sensor measurements are made, adding a new layer of privacy. In addition to balancing the privacy and utility of the captured data, we address trade-offs unique to miniature vision sensors, such as achieving high-quality field-of-view and resolution within the constraints of mass and volume. Our privacy preserving optics enable applications such as depth sensing, full-body motion tracking, people counting, blob detection and privacy preserving face recognition. While we demonstrate applications on macro-scale devices (smartphones, webcams, etc.) our theory has impact for smaller devices.

@InProceedings{Pittaluga_2015_CVPR,author = {Pittaluga, Francesco and Koppal, Sanjeev J.},title = {Privacy Preserving Optics for Miniature Vision Sensors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Sebastian Kaltwang, Sinisa Todorovic, Maja Pantic

Abstract: This paper is about estimating intensity levels of Facial Action Units (FAUs) in videos as an important and challenging step toward interpreting facial expressions. To address uncertainty in detections of facial landmark points, used as out input features, we formulate a new generative framework comprised of a graphical model, inference, and algorithms for learning both model parameters and structure. Our model is a latent tree (LT) that represents  input features of facial landmark points and FAU intensities as leaf nodes, and encodes their higher-order dependencies with latent nodes at tree levels closer to the root. No other restrictions are placed on the model structure beyond that it is a tree. We specify a new algorithm for efficient learning of model structure that iteratively builds LT by adding either new edge or new hidden node to LT, whichever of these two graph-edit operations gives the highest increase of the joint likelihood. Our structure learning efficiently computes likelihood increase and selects an optimal graph revision without considering all possible structural changes. For FAU intensity estimation, we derive closed-form expressions of posterior marginals of all variables in LT, and specify an efficient inference of in two passes -- bottom-up and top-down. Our evaluation on the benchmark DISFA and ShoulderPain datasets, in subject-independent setting, demonstrate that we outperform the state of the art, even in the presence of significant noise in locations of facial landmark points. We demonstrate our correct learning of model structure by probabilistically sampling facial landmark points, conditioned on a given FAU intensity, and thus generating plausible facial expressions.

@InProceedings{Kaltwang_2015_CVPR,author = {Kaltwang, Sebastian and Todorovic, Sinisa and Pantic, Maja},title = {Latent Trees for Estimating Intensity of Facial Action Units},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III

Abstract: Structural regularities in man-made environments reflect in the distribution of their surface normals. Describing these surface normal distributions is important in many computer vision applications, such as scene understanding, plane segmentation, and regularization of 3D reconstructions. Based on the small-variance limit of Bayesian nonparametric von-Mises-Fisher (vMF) mixture distributions, we propose two new flexible and efficient k-means-like clustering algorithms for directional data such as surface normals. The first, DP-vMF-means, is a batch clustering algorithm derived from the Dirichlet process (DP) vMF mixture. Recognizing the sequential nature of data collection in many applications, we extend this algorithm to DDP-vMF-means, which infers temporally evolving cluster structure from streaming data. Both algorithms naturally respect the geometry of directional data, which lies on the unit sphere. We demonstrate their performance on synthetic directional data and real 3D surface normals from RGB-D sensors. While our experiments focus on 3D data, both algorithms generalize to high dimensional directional data such as protein backbone configurations and semantic word vectors.

@InProceedings{Straub_2015_CVPR,author = {Straub, Julian and Campbell, Trevor and How, Jonathan P. and Fisher, III, John W.},title = {Small-Variance Nonparametric Clustering on the Hypersphere},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Deep Transfer Metric Learning

Abstract: Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML where the output of both the hidden layers and the top layer are optimized jointly. Experimental results on cross-dataset face verification and person re-identification validate the effectiveness of the proposed methods.

@InProceedings{Hu_2015_CVPR,author = {Hu, Junlin and Lu, Jiwen and Tan, Yap-Peng},title = {Deep Transfer Metric Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that can be tracked effectively through the whole tracking process. Specifically, we present a tracking reliability metric to measure how reliably a patch can be tracked, where a probability model is proposed to estimate the distribution of reliable patches under a sequential Monte Carlo framework. As the reliable patches distributed over the image, we exploit the motion trajectories to distinguish them from the background. Therefore, the visual object can be defined as the clustering of homo-trajectory patches, where a Hough voting-like scheme is employed to estimate the target state. Encouraging experimental results on a large set of sequences showed that the proposed approach is very effective and  in comparison to the state-of-the-art trackers. The full source code of our implementation will be publicly available.

@InProceedings{Li_2015_CVPR,author = {Li, Yang and Zhu, Jianke and Hoi, Steven C.H.},title = {Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Richard A. Newcombe, Dieter Fox, Steven M. Seitz

Abstract: We present the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our DynamicFusion approach reconstructs scene geometry whilst simultaneously estimating a dense volumetric 6D motion field that warps the estimated geometry into a live frame. Like KinectFusion, our system produces increasingly denoised, detailed, and complete reconstructions as more measurements are fused, and displays the updated model in real time. Because we do not require a template or other prior scene model, the approach is applicable to a wide range of moving objects and scenes.

@InProceedings{Newcombe_2015_CVPR,author = {Newcombe, Richard A. and Fox, Dieter and Seitz, Steven M.},title = {DynamicFusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu

Abstract: It is believed that eye movements in free-viewing of natural scenes are directed by both bottom-up visual saliency and top-down visual factors. In this paper, we propose a novel computational framework to simultaneously learn these two types of visual features from raw image data using a multiresolution convolutional neural network (Mr-CNN) for predicting eye fixations. The Mr-CNN is directly trained from image regions centered on fixation and non-fixation locations over multiple resolutions, using raw image pixels as inputs and eye fixation attributes as labels. Diverse top-down visual features can be learned in higher layers. Meanwhile bottom-up visual saliency can also be inferred via combining information over multiple resolutions. Finally, optimal integration of bottom-up and top-down cues can be learned in the last logistic regression layer to predict eye fixations. The proposed approach achieves state-of-the-art results over four publically available benchmark datasets, demonstrating the superiority of our work.

@InProceedings{Liu_2015_CVPR,author = {Liu, Nian and Han, Junwei and Zhang, Dingwen and Wen, Shifeng and Liu, Tianming},title = {Predicting Eye Fixations Using Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Kernel estimation for image deblurring is a challenging task and a large number of algorithms have been developed. Our hypothesis is that while individual kernels estimated using different methods alone are sometimes inadequate, they often complement each other. This paper addresses the problem of fusing multiple kernels  estimated using different methods into a more accurate one that can better support image deblurring than each individual kernel. In this paper, we develop a data-driven approach to kernel fusion that learns how each kernel contributes to the final kernel and how they interact with each other. We discuss various kernel fusion models and find that kernel fusion using Gaussian Conditional Random Fields performs best. This Gaussian Conditional Random Fields-based kernel fusion method not only models how individual kernels are fused at each kernel element but also the interaction of kernel fusion among multiple kernel elements. Our experiments show that our method can significantly improve image deblurring by combining kernels from multiple methods into a better one.

@InProceedings{Mai_2015_CVPR,author = {Mai, Long and Liu, Feng},title = {Kernel Fusion for Better Image Deblurring},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


George Papandreou, Iasonas Kokkinos, Pierre-Andre Savalle

Abstract: Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple 'max-pooling' (MP) layers. In this work we show that alternative methods of modeling deformations can improve the accuracy and efficiency of DCNNs. First, we introduce epitomic convolution as an alternative to the common convolution-MP cascade of DCNNs, that comes with the same computational cost but favorable learning properties. Second, we introduce a Multiple Instance Learning algorithm to accommodate global translation and scaling in image classification, yielding an efficient algorithm that trains and tests a DCNN in a consistent manner. Third we develop a DCNN sliding window detector that explicitly, but efficiently, searches over the object's position, scale, and aspect ratio. We provide competitive image classification and localization results on the ImageNet dataset and object detection results on Pascal VOC2007.

@InProceedings{Papandreou_2015_CVPR,author = {Papandreou, George and Kokkinos, Iasonas and Savalle, Pierre-Andre},title = {Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Christian Hane, Lubor Ladicky, Marc Pollefeys

Abstract: In this work we make use of recent advances in data driven classification to improve standard approaches for binocular stereo matching and single view depth estimation. Surface normal direction estimation has become feasible and shown to work reliably on state of the art benchmark datasets. Information about the surface orientation contributes crucial information about the scene geometry in cases where standard approaches struggle. We describe, how the responses of such a classifier can be included in global stereo matching approaches. One of the strengths of our approach is, that we can use the classifier responses for a whole set of directions and let the final optimization decide about the surface orientation. This is important in cases where based on the classifier, multiple different surface orientations seem likely. We evaluate our method on two challenging real-world datasets for the two proposed applications. For the binocular stereo matching we use road scene imagery taken from a car and for the single view depth estimation we use images taken in indoor environments.

@InProceedings{Hane_2015_CVPR,author = {Hane, Christian and Ladicky, Lubor and Pollefeys, Marc},title = {Direction Matters: Depth Estimation With a Surface Normal Classifier},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Sheng Huang, Mohamed Elhoseiny, Ahmed Elgammal, Dan Yang

Abstract: We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem, in which a collection of attribute projections is jointly learnt from the feature space to a hypergraph embedding space aligned with the attributes. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and N-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.

@InProceedings{Huang_2015_CVPR,author = {Huang, Sheng and Elhoseiny, Mohamed and Elgammal, Ahmed and Yang, Dan},title = {Learning Hypergraph-Regularized Attribute Predictors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yezhou Yang, Cornelia Fermuller, Yi Li, Yiannis Aloimonos

Abstract: The grasp type provides crucial information about human action. However, recognizing the grasp type in unconstrained scenes is challenging because of the large variations in appearance, occlusions and  geometric distortions. In this paper, first we present a convolutional neural network to classify functional hand grasp types. Experiments on a public static scene hand data set validate good performance of the presented method. Then we present two applications utilizing grasp type classification: (a) inference of human action intention and (b) fine level manipulation action segmentation. Experiments on both tasks demonstrate the usefulness of grasp type as a cognitive feature for computer vision. This study shows that the grasp type is a powerful symbolic representation for action understanding, and thus opens new avenues for future research.

@InProceedings{Yang_2015_CVPR,author = {Yang, Yezhou and Fermuller, Cornelia and Li, Yi and Aloimonos, Yiannis},title = {Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Roozbeh Mottaghi, Yu Xiang, Silvio Savarese

Abstract: Despite the fact that object detection, 3D pose estimation, and sub-category recognition are highly correlated tasks, they are usually addressed independently from each other because of the huge space of parameters. To jointly model all of these tasks, we propose a coarse-to-fine hierarchical representation, where each level of the hierarchy represents objects at a different level of granularity. The hierarchical representation prevents performance loss, which is often caused by the increase in the number of parameters (as we consider more tasks to model), and the joint modeling enables resolving ambiguities that exist in independent modeling of these tasks. We augment PASCAL 3D+ dataset with annotations for these tasks and show that our hierarchical model is effective in joint modeling of object detection, 3D pose estimation, and sub-category recognition.

@InProceedings{Mottaghi_2015_CVPR,author = {Mottaghi, Roozbeh and Xiang, Yu and Savarese, Silvio},title = {A Coarse-to-Fine Model for 3D Pose Estimation and Sub-Category Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ross Girshick, Forrest Iandola, Trevor Darrell, Jitendra Malik

Abstract: Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition.  They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are "black-box" non-linear classifiers.  In this paper, we show that a DPM can be formulated as a CNN, thus providing a synthesis of the two ideas.  Our construction involves unrolling the DPM inference algorithm and mapping each step to an equivalent CNN layer. From this perspective, it is natural to replace the standard image features used in DPMs with a learned feature extractor.  We call the resulting model a DeepPyramid DPM and experimentally validate it on PASCAL VOC object detection.  We find that DeepPyramid DPMs significantly outperform DPMs based on histograms of oriented gradients features (HOG) and slightly outperforms a comparable version of the recently introduced R-CNN detection system, while running significantly faster.

@InProceedings{Girshick_2015_CVPR,author = {Girshick, Ross and Iandola, Forrest and Darrell, Trevor and Malik, Jitendra},title = {Deformable Part Models are Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Content based image retrieval is highly relevant in medical imaging, since it makes vast amounts of imaging data accessible for comparison during diagnosis. Finding image similarity measures that reflect diagnostically relevant relationships is challenging, since the overall appearance variability is high compared to often subtle signatures of diseases. To learn models that capture the relationship between semantic clinical information and image elements at scale, we have to rely on data generated during clinical routine (images and radiology reports), since expert annotation is prohibitively costly. Here we show that re-mapping visual features extracted from medical imaging data based on weak labels that can be found in corresponding radiology reports creates descriptions of local image content capturing clinically relevant information. We show that these semantic profiles enable higher recall and precision during retrieval compared to visual features, and that we can even map semantic terms describing clinical findings from radiology reports to localized image volume areas.

@InProceedings{Hofmanninger_2015_CVPR,author = {Hofmanninger, Johannes and Langs, Georg},title = {Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call "fooling images" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.

@InProceedings{Nguyen_2015_CVPR,author = {Nguyen, Anh and Yosinski, Jason and Clune, Jeff},title = {Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik

Abstract: Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation. However, the information in this layer may be too coarse to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To get the best of both worlds, we define the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel. Using hypercolumns as pixel descriptors, we show results on three fine-grained localization tasks: simultaneous detection and segmentation[20], where we improve state-of-the-art from 49.7 mean AP^r[20] to 59.0, keypoint localization, where we get a 3.3 point boost over [19] and part labeling, where we show a 6.6 point gain over a strong baseline.

@InProceedings{Hariharan_2015_CVPR,author = {Hariharan, Bharath and Arbelaez, Pablo and Girshick, Ross and Malik, Jitendra},title = {Hypercolumns for Object Segmentation and Fine-Grained Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Stephan Schraml, Ahmed Nabil Belbachir, Horst Bischof

Abstract: This paper presents a stereo matching approach for a novel multi-perspective panoramic stereo vision system, making use of asynchronous and non-simultaneous stereo imaging towards real-time 3D 360deg vision. The method is designed for events representing the scenes visual contrast as a sparse visual code allowing the stereo reconstruction of high resolution panoramic views. We propose a novel cost measure for the stereo matching, which makes use of a similarity measure based on event distributions. Thus, the robustness to variations in event occurrences was increased. An evaluation of the proposed stereo method is presented using distance estimation of panoramic stereo views and ground truth data. Furthermore, our approach is compared to standard stereo methods applied on event-data. Results show that we obtain 3D reconstructions of 1024 x 3600 round views and outperform depth reconstruction accuracy of state-of-the-art methods on event data.

@InProceedings{Schraml_2015_CVPR,author = {Schraml, Stephan and Nabil Belbachir, Ahmed and Bischof, Horst},title = {Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hangfan Liu, Ruiqin Xiong, Jian Zhang, Wen Gao

Abstract: This paper proposes a new image denoising approach using adaptive signal modeling and adaptive soft-thresholding. It improves the image quality by regularizing all the patches in image based on distribution modeling in transform domain. Instead of using a global model for all patches, it employs content adaptive models to address the non-stationarity of image signals. The distribution model of each patch is estimated individually and can vary for different transform bands and for different patch locations. In particular, we allow the distribution model for each individual patch to have non-zero expectation. To estimate the expectation and variance parameters for the transform bands of a particular patch, we exploit the non-local correlation of image and collect a set of similar patches as data samples to form the distribution. Irrelevant patches are excluded so that this non-local based modeling is more accurate than global modeling. Adaptive soft-thresholding is employed since we observed that the distribution of non-local samples can be approximated by Laplacian distribution. Experimental results show that the proposed scheme outperforms the state-of-the-art denoising methods such as BM3D and CSR in both the PSNR and the perceptual quality.

@InProceedings{Liu_2015_CVPR,author = {Liu, Hangfan and Xiong, Ruiqin and Zhang, Jian and Gao, Wen},title = {Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We show how the simplex algorithm can be tailored to the linear programming relaxation of pairwise energy minimization with binary variables. A special structure formed by basic and nonbasic variables in each stage of the algorithm is identified and utilized to perform the whole iterative process combinatorially over the input energy minimization graph rather than algebraically over the simplex tableau. This leads to a new efficient solver. We demonstrate that for some computer vision instances it performs even better than methods reducing binary energy minimization to finding maximum flow in a network.

@InProceedings{Prusa_2015_CVPR,author = {Prusa, Daniel},title = {Graph-Based Simplex Method for Pairwise Energy Minimization With Binary Variables},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Nested Motion Descriptors

Abstract: A nested motion descriptor is a spatiotemporal representation of motion that is invariant to global camera translation, without requiring an explicit estimate of optical flow or camera stabilization.  This descriptor is a natural spatiotemporal extension of the nested shape descriptor to the representation of motion.  We demonstrate that the quadrature steerable pyramid can be used to pool phase, and that pooling phase rather than magnitude provides an estimate of camera motion.  This motion can be removed using the log-spiral normalization as introduced in the nested shape descriptor.  Furthermore, this structure enables an elegant visualization of salient motion using the reconstruction properties of the steerable pyramid.  We compare our descriptor to local motion descriptors, HOG-3D and HOG-HOF, and show improvements on three activity recognition datasets.

@InProceedings{Byrne_2015_CVPR,author = {Byrne, Jeffrey},title = {Nested Motion Descriptors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Mingsong Dou, Jonathan Taylor, Henry Fuchs, Andrew Fitzgibbon, Shahram Izadi

Abstract: We present a 3D scanning system for deformable objects that uses only a single Kinect sensor. Our work allows considerable amount of nonrigid deformations during scanning, and achieves high quality results without heavily constraining user or camera motion. We do not rely on any prior shape knowledge, enabling general object scanning with freeform deformations. To deal with the drift problem when nonrigidly aligning the input sequence, we automatically detect loop closures, distribute the alignment error over the loop, and finally use a bundle adjustment algorithm to optimize for the latent 3D shape and nonrigid deformation parameters simultaneously. We demonstrate high quality scanning results in some challenging sequences, comparing with state of art nonrigid techniques, as well as ground truth data.

@InProceedings{Dou_2015_CVPR,author = {Dou, Mingsong and Taylor, Jonathan and Fuchs, Henry and Fitzgibbon, Andrew and Izadi, Shahram},title = {3D Scanning Deformable Objects With a Single RGBD Sensor},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiaolong Wang, David Fouhey, Abhinav Gupta

Abstract: In the past few years, convolutional neural nets (CNN) have shown incredible promise for learning visual representations. In this paper, we use CNNs for the task of predicting surface normals from a single image. But what is the right architecture? We propose to build upon the decades of hard work in 3D scene understanding to design a new CNN architecture for the task of surface normal estimation. We show that incorporating several constraints (man-made, Manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation. We also show that our network is quite robust and show state of the art results on other datasets as well without any fine-tuning.

@InProceedings{Wang_2015_CVPR,author = {Wang, Xiaolong and Fouhey, David and Gupta, Abhinav},title = {Designing Deep Networks for Surface Normal Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Gottfried Graber, Jonathan Balzer, Stefano Soatto, Thomas Pock

Abstract: We propose a method for dense three-dimensional surface reconstruction that leverages the strengths of shape-based approaches, by imposing regularization that respects the geometry of the surface, and the strength of depth-map-based stereo, by avoiding costly computation of surface topology. The result is a near real-time variational reconstruction algorithm free of the staircasing artifacts that affect depth-map and plane-sweeping approaches. This is made possible by exploiting the gauge ambiguity to design a novel representation of the regularizer that is linear in the parameters and hence amenable to be optimized with state-of-the-art primal-dual numerical schemes.

@InProceedings{Graber_2015_CVPR,author = {Graber, Gottfried and Balzer, Jonathan and Soatto, Stefano and Pock, Thomas},title = {Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy

Abstract: We consider MAP-inference for graphical models and propose a novel efficient algorithm for finding persistent labels. Our algorithm marks each label in each node of the considered graphical model either as (i) optimal, meaning that it belongs to all optimal solutions of the inference problem; (ii) non-optimal if it provably does not belong to any solution; or (iii)  undefined, which means our algorithm can not make a decision regarding the label. Moreover, we prove optimality of our approach, that it delivers in a certain sense the largest total number of labels marked as optimal or non-optimal. We demonstrate superiority of our approach on problems from machine learning and computer vision benchmarks.

@InProceedings{Shekhovtsov_2015_CVPR,author = {Shekhovtsov, Alexander and Swoboda, Paul and Savchynskyy, Bogdan},title = {Maximum Persistency via Iterative Relaxed Inference With Graphical Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Layered RGBD Scene Flow Estimation

Deqing Sun, Erik B. Sudderth, Hanspeter Pfister

Abstract: As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention. Although the depth information allows the recovery of 3D motion from a single view, it poses new challenges. In particular, depth boundaries are not well-aligned with RGB image edges and therefore not reliable cues to localize 2D motion boundaries. In addition,  methods that extend the 2D optical flow formulation to 3D still produce large errors in occlusion regions. To better use depth for occlusion reasoning, we propose a layered RGBD scene flow method that jointly solves for the scene segmentation and the motion. Our key observation is that the noisy depth is sufficient to decide the depth ordering of layers, thereby avoiding a computational bottleneck for RGB layered methods. Furthermore, the depth  enables us to estimate a per-layer 3D rigid motion to constrain the motion of each layer. Experimental results on both the Middlebury and real-world sequences demonstrate the effectiveness of the layered approach for RGBD scene flow estimation.

@InProceedings{Sun_2015_CVPR,author = {Sun, Deqing and Sudderth, Erik B. and Pfister, Hanspeter},title = {Layered RGBD Scene Flow Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abhishek Sharma, Oncel Tuzel, David W. Jacobs

Abstract: This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse trees. This improves the feature representation of every super-pixel in the image for better classification into semantic categories. We analyze RCPN and propose two novel contributions to further improve the model. We first analyze the learning of RCPN parameters and discover the presence of bypass error paths in the computation graph of RCPN that can hinder contextual propagation. We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function. Secondly, we use an MRF on the parse tree nodes to model the hierarchical dependency present in the output. Both modifications provide performance boosts over the original RCPN and the new system achieves state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler urban datasets.

@InProceedings{Sharma_2015_CVPR,author = {Sharma, Abhishek and Tuzel, Oncel and Jacobs, David W.},title = {Deep Hierarchical Parsing for Semantic Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chen Fang, Hailin Jin, Jianchao Yang, Zhe Lin

Abstract: Image feature representation plays an essential role in image recognition and related tasks. The current state-of-the-art feature learning paradigm is supervised learning from labeled data. However, this paradigm requires large-scale category labels, which limits its applicability to domains where labels are hard to obtain. In this paper, we propose a new data-driven feature learning paradigm which does not rely on category labels. Instead, we learn from user behavior data collected on social media. Concretely, we use the image relationship discovered in the latent space from the user behavior data to guide the image feature learning. We collect a large-scale image and user behavior dataset from Behance.net. The dataset consists of 1.9 million images and over 300 million view records from 1.9 million users. We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities. We also show that the learned feature performs competitively on various recognition benchmarks.

@InProceedings{Fang_2015_CVPR,author = {Fang, Chen and Jin, Hailin and Yang, Jianchao and Lin, Zhe},title = {Collaborative Feature Learning From Social Media},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Shuran Song, Samuel P. Lichtenberg, Jianxiong Xiao

Abstract: Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.

@InProceedings{Song_2015_CVPR,author = {Song, Shuran and Lichtenberg, Samuel P. and Xiao, Jianxiong},title = {SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang

Abstract: In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering into the multi-view domain, and utilize the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term to explore the complementarity of multi-view representations, which could be solved efficiently by using the alternating minimizing optimization. Compared to other multi-view clustering methods, the enhanced complementarity reduces the redundancy between the multi-view features, and improves the accuracy of the clustering results. Experiments on both image and video face clustering well demonstrate that the proposed method outperforms the state-of-the-art methods.

@InProceedings{Cao_2015_CVPR,author = {Cao, Xiaochun and Zhang, Changqing and Fu, Huazhu and Liu, Si and Zhang, Hua},title = {Diversity-Induced Multi-View Subspace Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Hashing With Binary Autoencoders

Miguel A. Carreira-Perpinan, Ramin Raziperchikolaei

Abstract: An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approaches approximate the optimization by relaxing the constraints and then binarizing the result. Here, we focus on the binary autoencoder model, which seeks to reconstruct an image from the binary code produced by the hash function. We show that the optimization can be simplified with the method of auxiliary coordinates. This reformulates the optimization as alternating two easier steps: one that learns the encoder and decoder separately, and one that optimizes the code for each image. Image retrieval experiments show the resulting hash function outperforms or is competitive with state-of-the-art methods for binary hashing.

@InProceedings{Carreira-Perpinan_2015_CVPR,author = {Carreira-Perpinan, Miguel A. and Raziperchikolaei, Ramin},title = {Hashing With Binary Autoencoders},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, Serge Belongie

Abstract: We introduce tools and methodologies to collect high quality, large scale fine-grained computer vision datasets using citizen scientists -- crowd annotators who are passionate and knowledgeable about specific domains such as birds or airplanes.  We worked with citizen scientists and domain experts to collect NABirds, a new high quality dataset containing 48,562 images of North American birds with 555 categories, part annotations and bounding boxes.  We find that citizen scientists are significantly more accurate than Mechanical Turkers at zero cost.  We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%.  Nevertheless, we found that learning algorithms are surprisingly robust to annotation errors and this level of training data corruption can lead to an acceptably small increase in test error if the training set has sufficient size.  At the same time, we found that an expert-curated high quality test set like NABirds is necessary to accurately measure the performance of fine-grained computer vision systems. We used NABirds to train a publicly available bird recognition service deployed on the web site of the Cornell Lab of Ornithology.

@InProceedings{Horn_2015_CVPR,author = {Van Horn, Grant and Branson, Steve and Farrell, Ryan and Haber, Scott and Barry, Jessie and Ipeirotis, Panos and Perona, Pietro and Belongie, Serge},title = {Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Light Field Layer Matting

Juliet Fiss, Brian Curless, Rick Szeliski

Abstract: In this paper, we use matting to separate foreground layers from light fields captured with a plenoptic camera. We represent the input 4D light field as a 4D background light field, plus a 2D spatially varying foreground color layer with alpha. Our method can be used to both pull a fore- ground matte and estimate an occluded background light field. Our method assumes that the foreground layer is thin and fronto-parallel, and is composed of a limited set of col- ors that are distinct from the background layer colors. Our method works well for thin, translucent, and blurred foreground occluders. Our representation can be used to render the light field from novel views, handling disocclusions while avoiding common artifacts.

@InProceedings{Fiss_2015_CVPR,author = {Fiss, Juliet and Curless, Brian and Szeliski, Rick},title = {Light Field Layer Matting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Depth Camera Tracking With Contour Cues

Abstract: We present an approach for tracking camera pose in real time given a stream of depth images. Existing algorithms are prone to drift in the presence of smooth surfaces that destabilize geometric alignment. We show that useful contour cues can be extracted from noisy and incomplete depth input. These cues are used to establish correspondence constraints that carry information about scene geometry and constrain pose estimation. Despite ambiguities in the input, the presented contour constraints reliably improve tracking accuracy. Results on benchmark sequences and on additional challenging examples demonstrate the utility of contour cues for real-time camera pose estimation.

@InProceedings{Zhou_2015_CVPR,author = {Zhou, Qian-Yi and Koltun, Vladlen},title = {Depth Camera Tracking With Contour Cues},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu

Abstract: We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities. Unlike previous approaches that only reason locally, we propose to exploit the global structure of the scene to estimate its depth. To this end, we introduce a hierarchical representation of the scene, which models local depth jointly with mid-level and global scene structures. We formulate single image depth estimation as inference in a graphical model whose edges let us encode the interactions within and across the different layers of our hierarchy. Our method therefore still produces detailed depth estimates, but also leverages higher-level information about the scene. We demonstrate the benefits of our approach over local depth estimation methods on standard indoor datasets.

@InProceedings{Zhuo_2015_CVPR,author = {Zhuo, Wei and Salzmann, Mathieu and He, Xuming and Liu, Miaomiao},title = {Indoor Scene Structure Analysis for Single Image Depth Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Radial Distortion Homography

Zuzana Kukelova, Jan Heller, Martin Bujnak, Tomas Pajdla

Abstract: The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more.  Ignoring the radial distortion component in homography estimation---even for classical perspective cameras---may lead to significant errors or totally wrong estimates. In this paper, we fill the gap among the homography estimation methods by presenting two algorithms for estimating homography between two cameras with different radial distortions. Both algorithms can handle planar scenes as well as scenes where the relative motion between the cameras is a pure rotation. The first algorithm uses the minimal number of five image point correspondences and solves a nonlinear system of polynomial equations using the Groebner basis method. The second algorithm uses a non-minimal number of six image point correspondences and leads to a simple system of two quadratic equations in two unknowns and one system of six linear equations. The proposed algorithms are fast, stable, and can be efficiently used inside a RANSAC loop.

@InProceedings{Kukelova_2015_CVPR,author = {Kukelova, Zuzana and Heller, Jan and Bujnak, Martin and Pajdla, Tomas},title = {Radial Distortion Homography},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Miaojing Shi, Yannis Avrithis, Herve Jegou

Abstract: Recent works show that image comparison based on local descriptors is corrupted by visual bursts, which tend to dominate the image similarity. The existing strategies, like power-law normalization, improve the results by discounting the contribution of visual bursts to the image similarity.  In this paper, we propose to explicitly detect the visual bursts in an image at an early stage. We compare several detection strategies jointly taking into account feature similarity and geometrical quantities. The bursty groups are merged into meta-features, which are used as input to state-of-the-art image search systems such as VLAD or the selective match kernel.  Then, we show the interest of using this strategy in an asymmetrical manner, with only the database features being aggregated but not those of the query.  Extensive experiments performed on public benchmarks for visual retrieval show the benefits of our method, which achieves performance on par with the state of the art but with a significantly reduced complexity, thanks to the lower number of features fed to the indexing system.

@InProceedings{Shi_2015_CVPR,author = {Shi, Miaojing and Avrithis, Yannis and Jegou, Herve},title = {Early Burst Detection for Memory-Efficient Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, Christoph Bregler

Abstract: Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introduce invariance and prevent over-training. These benefits of pooling come at the cost of reduced localization accuracy. We introduce a novel architecture which includes an efficient `position refinement' model that is trained to estimate the joint offset location within a small region of the image. This refinement model is jointly trained in cascade with a state-of-the-art ConvNet model to achieve improved accuracy in human joint location estimation. We show that the variance of our detector approaches the variance of human annotations on the FLIC dataset and outperforms all existing approaches on the MPII-human-pose dataset.

@InProceedings{Tompson_2015_CVPR,author = {Tompson, Jonathan and Goroshin, Ross and Jain, Arjun and LeCun, Yann and Bregler, Christoph},title = {Efficient Object Localization Using Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}