Abstract: Human actions are, inherently, structured patterns of body movements. We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition. The hierarchical spatio-temporal trees provide a robust mid-level representation for actions. However, discovery of frequent and discriminative tree structures is challenging due to the exponential search space, particularly if one allows partial matching. We address this by first building a concise action vocabulary via discriminative clustering. Using the action vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of highly discriminative tree patterns. We show that these tree patterns, alone, or in combination with shorter patterns (action words and pair-wise patterns) achieve state-of-the-art  performance on two challenging datasets: UCF Sports and HighFive. Moreover, trees learned on HighFive are used in recognizing two action classes in a different dataset, Hollywood3D, demonstrating the potential for cross-dataset generality of the trees our approach discovers.

@InProceedings{Ma_2015_CVPR,author = {Ma, Shugao and Sigal, Leonid and Sclaroff, Stan},title = {Space-Time Tree Ensemble for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Siyu Tang, Bjoern Andres, Miykhaylo Andriluka, Bernt Schiele

Abstract: Tracking multiple targets in a video, based on a finite set of detection hypotheses, is a persistent problem in computer vision. A common strategy for tracking is to first select hypotheses spatially and then to link these over time while maintaining disjoint path constraints. In crowded scenes multiple hypotheses will often be similar to each other making selection of optimal links an unnecessary hard optimization problem due to the sequential treatment of space and time. Embracing this observation, we propose to link and cluster plausible detections jointly across space and time. Specifically, we state multi-target tracking as a Minimum Cost Subgraph Multicut Problem. Evidence about pairs of detection hypotheses is incorporated whether the detections are in the same frame, neighboring frames or distant frames. This facilitates long-range re-identification and within-frame clustering. Results for published benchmark sequences demonstrate the superiority of this approach.

@InProceedings{Tang_2015_CVPR,author = {Tang, Siyu and Andres, Bjoern and Andriluka, Miykhaylo and Schiele, Bernt},title = {Subgraph Decomposition for Multi-Target Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xian-Ming Liu, Rongrong Ji, Changhu Wang, Wei Liu, Bineng Zhong, Thomas S. Huang

Abstract: Exploring image structure is a long-standing yet important research subject in the computer vision community. In this paper, we focus on understanding image structure inspired by the "simple-to-complex" biological evidence. A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space. To improve the robustness and flexibility of image representation, we further bundle the image appearances into hierarchical parsing trees. Image descriptions are subsequently constructed by performing a structural pooling, facilitating efficient matching between the parsing trees. We leverage the proposed hierarchical shape parsing to study two exemplar applications including edge scale refinement and unsupervised "objectness" detection. We show competitive parsing performance comparing to the state-of-the-arts in above scenarios with far less proposals, which thus demonstrates the advantage of the proposed parsing scheme.

@InProceedings{Liu_2015_CVPR,author = {Liu, Xian-Ming and Ji, Rongrong and Wang, Changhu and Liu, Wei and Zhong, Bineng and Huang, Thomas S.},title = {Understanding Image Structure via Hierarchical Shape Parsing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yanchao Yang, Zhaojin Lu, Ganesh Sundaramoorthi

Abstract: We present a new approach to wide baseline matching. We propose to use a hierarchical decomposition of the image domain and coarse-to-fine selection of regions to match. In contrast to interest point matching methods, which sample salient regions to reduce the cost of comparing all regions in two images, our method eliminates regions systematically to achieve efficiency. One advantage of our approach is that it is not restricted to covariant salient regions, which is too restrictive under large viewpoint and leads to few corresponding regions. Affine invariant matching of regions in the hierarchy is achieved efficiently by a coarse-to-fine search of the affine space. Experiments on two benchmark datasets shows that our method finds more correct correspondence of the image (with fewer false alarms) than other wide baseline methods on large viewpoint change.

@InProceedings{Yang_2015_CVPR,author = {Yang, Yanchao and Lu, Zhaojin and Sundaramoorthi, Ganesh},title = {Coarse-To-Fine Region Selection and Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yumin Suh, Kamil Adamczewski, Kyoung Mu Lee

Abstract: Feature correspondence plays a central role in various computer vision applications. It is widely formulated as a graph matching problem due to its robust performance under challenging conditions, such as background clutter, object deformation and repetitive patterns. A variety of fast and accurate algorithms  have been proposed for graph matching. However, most of them focus on improving the recall of the solution while rarely considering its precision, thus inducing a solution with numerous outliers. To address both precision and recall feature correspondence should rather be formulated as a subgraph matching problem. This paper proposes a new subgraph matching formulation which uses a compactness prior, an additional constraint that prefers sparser solutions and effectively eliminates outliers. To solve the new optimization problem, we propose a meta-algorithm based on Markov chain Monte Carlo. By constructing Markov chain on the restricted search space instead of the original solution space, our method approximates the solution effectively. The experiments indicate that our proposed formulation and algorithm significantly improve the baseline performance under challenging conditions when both outliers and deformation noise are present.

@InProceedings{Suh_2015_CVPR,author = {Suh, Yumin and Adamczewski, Kamil and Mu Lee, Kyoung},title = {Subgraph Matching Using Compactness Prior for Robust Feature Correspondence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Recently, an increasing number of works have proposed to learn visual saliency by leveraging human fixations. However, the collection of human fixations is time consuming and the existing eye tracking datasets are generally small when compared with other domains. Thus, it contains a certain degree of dataset bias due to the large image variations (e.g., outdoor scenes vs. emotion-evoking images). In the learning based saliency prediction literature, most models are trained and evaluated within the same dataset and cross dataset validation is not yet a common practice. Instead of directly applying model learned from another dataset in cross dataset fashion, it is better to transfer the prior knowledge obtained from one dataset to improve the training and prediction on another. In addition, since new datasets are built and shared in the community from time to time, it would be good not to retrain the entire model when new data are added. To address these problems, we proposed a new learning based saliency model, namely Label Consistent Quadratic Surrogate algorithm, which employs an iterative online algorithm to learn a sparse dictionary with label consistent constraint. The advantages of the proposed model are three-folds: (1) the quadratic surrogate function guarantees convergence at each iteration, (2) the label consistent constraint enforces the predicted sparse code to be discriminative, and (3) the online properties enable the proposed algorithm to adapt existing model with new data without retraining. As shown in this work, the proposed saliency model achieves better performance than the state-of-the-art saliency models.

@InProceedings{Luo_2015_CVPR,author = {Luo, Yan and Wong, Yongkang and Zhao, Qi},title = {Label Consistent Quadratic Surrogate Model for Visual Saliency Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang

Abstract: Deep learning methods have achieved great success in pedestrian detection, owing to its ability to learn features from raw pixels. However, they can mainly capture middlelevel representations, such as pose of pedestrian, but confuses positive with hard negative samples (Fig.1 (a)), which have large ambiguity and can only be distinguished by highlevel representation. To address this ambiguity, this work jointly optimize pedestrian detection with semantic tasks, including pedestrian attributes (e.g. 'carrying backpack') and scene attributes (e.g. 'vehicle', 'tree', and 'horizontal'). Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources. Since distinct tasks have distinct convergence rates and data from different datasets have different distributions, a multi-task objective function is carefully designed to coordinate tasks and reduce discrepancies among datasets. The importance coefficients of tasks and network parameters in this objective function can be iteratively estimated. Extensive evaluations show that the proposed approach outperforms the state-of-the-art on the challenging Caltech [10] and ETH [11] datasets where it reduces the miss rates of previous deep models by 17 and 5.5 percent, respectively.

@InProceedings{Tian_2015_CVPR,author = {Tian, Yonglong and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},title = {Pedestrian Detection Aided by Deep Learning Semantic Tasks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim

Abstract: The notion of multihypothesis trajectory analysis (MTA) for robust visual tracking is proposed in this work. We employ multiple component trackers using texture, color, and illumination invariant features, respectively. Each component tracker traces a target object forwardly and then backwardly over a time interval. By analyzing the pair of the forward and backward trajectories, we measure the robustness of the component tracker. To this end, we extract the geometry similarity, the cyclic weight, and the appearance similarity from the forward and backward trajectories. We select the optimal component tracker to yield the maximum robustness score, and use its forward trajectory as the final tracking result. Experimental results show that the proposed MTA tracker improves the robustness and the accuracy of tracking, outperforming the state-of-the-art trackers on a recent benchmark dataset.

@InProceedings{Lee_2015_CVPR,author = {Lee, Dae-Youn and Sim, Jae-Young and Kim, Chang-Su},title = {Multihypothesis Trajectory Analysis for Robust Visual Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor, which we call DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training.

@InProceedings{Dong_2015_CVPR,author = {Dong, Jingming and Soatto, Stefano},title = {Domain-Size Pooling in Local Descriptors: DSP-SIFT},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Junjie Yan, Yinan Yu, Xiangyu Zhu, Zhen Lei, Stan Z. Li

Abstract: Object detection is always conducted by object proposal generation and classification sequentially. This paper handles object detection in a superpixel oriented manner instead of the proposal oriented. Specially, this paper takes object detection as a multi-label superpixel labeling problem by minimizing an energy function. It uses the data cost term to capture the appearance, smooth cost term to encode the spatial context and label cost term to favor compact detection. The data cost is learned through a convolutional neural network and the parameters in the labeling model are learned through a structural SVM. Compared with proposal generation and classification based methods, the proposed superpixel labeling method can naturally detect objects missed by proposal generation step and capture the global image context to infer the overlapping objects.   The proposed method shows its advantage in Pascal VOC and ImageNet. Notably, it performs better than the ImageNet ILSVRC2014 winner GoogLeNet (45.0% V.S. 43.9% in mAP) with much shallower and fewer CNNs.

@InProceedings{Yan_2015_CVPR,author = {Yan, Junjie and Yu, Yinan and Zhu, Xiangyu and Lei, Zhen and Li, Stan Z.},title = {Object Detection by Labeling Superpixels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Fast 2D Border Ownership Assignment

Ching Teo, Cornelia Fermuller, Yiannis Aloimonos

Abstract: A method for efficient border ownership assignment in 2D images is proposed. Leveraging on recent advances using Structured Random Forests (SRF) for boundary detection, we impose a novel border ownership structure that detects both boundaries and border ownership at the same time. Key to this work are features that predict ownership cues from 2D images. To this end, we use several different local cues: shape, spectral properties of boundary patches, and semi-global grouping cues that are indicative of perceived depth. For shape, we use HoG-like descriptors that encode local curvature (convexity and concavity). For spectral properties, such as extremal edges, we first learn an orthonormal basis spanned by the top K eigenvectors via PCA over common types of contour tokens. For grouping, we introduce a novel mid-level descriptor that captures patterns near edges and indicates ownership information of the boundary. Experimental results over a subset of the Berkeley Segmentation Dataset (BSDS) and the NYU Depth V2 dataset show that our method's performance exceeds current state-of-the-art multi-stage approaches that use more complex features.

@InProceedings{Teo_2015_CVPR,author = {Teo, Ching and Fermuller, Cornelia and Aloimonos, Yiannis},title = {Fast 2D Border Ownership Assignment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Johannes L. Schonberger, Filip Radenovic, Ondrej Chum, Jan-Michael Frahm

Abstract: Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade. This impressive progress can be in part attributed to the introduction of efficient retrieval methods for those systems. While this boosts scalability, it also limits the amount of detail that the large-scale reconstruction systems are able to produce. In this paper, we propose a joint reconstruction and retrieval system that maintains the scalability of large-scale Structure-from-Motion systems while also recovering the often lost ability of reconstructing fine details of the scene. We demonstrate our proposed method on a large-scale dataset of 7.4 million images downloaded from the Internet.

@InProceedings{Schonberger_2015_CVPR,author = {Schonberger, Johannes L. and Radenovic, Filip and Chum, Ondrej and Frahm, Jan-Michael},title = {From Single Image Query to Detailed 3D Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Felix Heide, Wolfgang Heidrich, Gordon Wetzstein

Abstract: Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.

@InProceedings{Heide_2015_CVPR,author = {Heide, Felix and Heidrich, Wolfgang and Wetzstein, Gordon},title = {Fast and Flexible Convolutional Sparse Coding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Thalaiyasingam Ajanthan, Richard Hartley, Mathieu Salzmann, Hongdong Li

Abstract: While widely acknowledged as highly effective in computer vision, multi-label MRFs with non-convex priors are difficult to optimize. To tackle this, we introduce an algorithm that iteratively approximates the original energy with an appropriately weighted surrogate energy that is easier to minimize. Our algorithm guarantees that the original energy decreases at each iteration. In particular, we consider the scenario where the global minimizer of the weighted surrogate energy can be obtained by a multi-label graph cut algorithm, and show that our algorithm then lets us handle of large variety of non-convex priors. We demonstrate the benefits of our method over state-of-the-art MRF energy minimization techniques on stereo and inpainting problems.

@InProceedings{Ajanthan_2015_CVPR,author = {Ajanthan, Thalaiyasingam and Hartley, Richard and Salzmann, Mathieu and Li, Hongdong},title = {Iteratively Reweighted Graph Cut for Multi-Label MRFs With Non-Convex Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xinchao Li, Martha Larson, Alan Hanjalic

Abstract: Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. In this paper, we consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. In addition, we combine the information on geometric relations from both the individual correspondences and pairs of correspondences to further improve the verification accuracy. Experimental results on three reference datasets show that the proposed approach results in a substantial performance improvement compared to the existing methods, without making concessions regarding computational efficiency.

@InProceedings{Li_2015_CVPR,author = {Li, Xinchao and Larson, Martha and Hanjalic, Alan},title = {Pairwise Geometric Matching for Large-Scale Object Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We consider the problem of depth estimation from a single monocular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspondences, motions etc. Previous efforts have been focusing on exploiting geometric priors or additional sources of information, with all using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) are setting new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimations can be naturally formulated into a continuous conditional random field (CRF) learning problem. Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF. Specifically, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. The proposed method can be used for depth estimations of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be analytically calculated, thus we can exactly solve the log-likelihood optimization. Moreover, solving the MAP problem for predicting depths of a new image is highly efficient as closed-form solutions exist. We experimentally demonstrate that the proposed method outperforms state-of-the-art depth estimation methods on both indoor and outdoor scene datasets.

@InProceedings{Liu_2015_CVPR,author = {Liu, Fayao and Shen, Chunhua and Lin, Guosheng},title = {Deep Convolutional Neural Fields for Depth Estimation From a Single Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xianming Liu, Xiaolin Wu, Jiantao Zhou, Debin Zhao

Abstract: Arguably the most common cause of image degradation is compression.  This papers presents a novel approach to restoring JPEG-compressed images.  The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images.  The restoration is a sparse coding process carried out jointly in the DCT and pixel domains.  The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time using on-line machine-learnt local spatial features to regulate the solution of the underlying inverse problem.  Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.

@InProceedings{Liu_2015_CVPR,author = {Liu, Xianming and Wu, Xiaolin and Zhou, Jiantao and Zhao, Debin},title = {Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yale Song, Jordi Vallmitjana, Amanda Stent, Alejandro Jaimes

Abstract: Video summarization is a challenging problem in part because knowing which part of a video is important requires prior knowledge about its main topic. We present TVSum, an unsupervised video summarization framework that uses title-based image search results to find visually important shots. We observe that a video title is often carefully chosen to be maximally descriptive of its main topic, and hence images related to the title can serve as a proxy for important visual concepts of the main topic. However, because titles are free-formed, unconstrained, and often written ambiguously, images searched using the title can contain noise (images irrelevant to video content) and variance (images of different topics). To deal with this challenge, we developed a novel co-archetypal analysis technique that learns canonical visual concepts shared between video and images, but not in either alone, by finding a joint-factorial representation of two data sets. We introduce a new benchmark dataset, TVSum50, that contains 50 videos and their shot-level importance scores annotated via crowdsourcing. Experimental results on two datasets, SumMe and TVSum50, suggest our approach produces superior quality summaries compared to several recently proposed approaches.

@InProceedings{Song_2015_CVPR,author = {Song, Yale and Vallmitjana, Jordi and Stent, Amanda and Jaimes, Alejandro},title = {TVSum: Summarizing Web Videos Using Titles},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited. In this paper we conduct a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself? To answer this question we contribute a general framework to invert representations. We show that this method can invert representations such as HOG more accurately than recent alternatives while being applicable to CNNs too. We then use this technique to study the inverse of recent state-of-the-art CNN image representations for the first time. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.

@InProceedings{Mahendran_2015_CVPR,author = {Mahendran, Aravindh and Vedaldi, Andrea},title = {Understanding Deep Image Representations by Inverting Them},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jia-Bin Huang, Abhishek Singh, Narendra Ahuja

Abstract: Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing results without extensive training on external databases. Such algorithms exploit the statistical prior that patches in a natural image tend to recur within and across scales of the same image. However, the internal dictionary obtained from the given image may not always be sufficiently expressive to cover the textural appearance variations in the scene. In this paper, we extend self-similarity based SR to overcome this drawback. We expand the internal patch search space by allowing geometric variations. We do so by explicitly localizing planes in the scene and using the detected perspective geometry to guide the patch search process. We also incorporate additional affine transformations to accommodate local shape variations. We propose a compositional model to simultaneously handle both types of transformations. We extensively evaluate the performance in both urban and natural scenes. Even without using any external training databases, we achieve significantly superior results on urban scenes, while maintaining comparable performance on natural scenes as other state-of-the-art SR algorithms.

@InProceedings{Huang_2015_CVPR,author = {Huang, Jia-Bin and Singh, Abhishek and Ahuja, Narendra},title = {Single Image Super-Resolution From Transformed Self-Exemplars},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Markus Schoeler, Jeremie Papon, Florentin Worgotter

Abstract: While humans can easily separate unknown objects into meaningful parts, recent segmentation methods can only achieve similar partitionings by training on human-annotated ground-truth data. Here we introduce a bottom-up method for segmenting 3D point clouds into functional parts which does not require supervision and achieves equally good results. Our method uses local concavities as an indicator for inter-part boundaries. We show that this criterion is efficient to compute and generalizes well across different object classes. The algorithm employs a novel locally constrained geometrical boundary model which proposes greedy cuts through a local concavity graph. Only planar cuts are considered and evaluated using a cost function, which rewards cuts orthogonal to concave edges. Additionally, a local clustering constraint is applied to ensure the partitioning only affects relevant locally concave regions. We evaluate our algorithm on recordings from an RGB-D camera as well as the Princeton Segmentation Benchmark, using a fixed set of parameters across all object classes. This stands in stark contrast to most reported results which require either knowing the number of parts or annotated ground-truth for learning. Our approach outperforms all existing bottom-up methods (reducing the gap to human performance by up to 50%) and achieves scores similar to top-down data-driven approaches.

@InProceedings{Schoeler_2015_CVPR,author = {Schoeler, Markus and Papon, Jeremie and Worgotter, Florentin},title = {Constrained Planar Cuts - Object Partitioning for Point Clouds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: There is an emerging interest on using high-dimensional datasets beyond 2D images in saliency detection. Examples include 3D data based on stereo matching and Kinect sensors and more recently 4D light field data. However, these techniques adopt very different solution frameworks, in both type of features and procedures on using them. In this paper, we present a unified saliency detection framework for handling heterogenous types of input data. Our approach builds dictionaries using data-specific features. Specifically, we first select a group of potential background superpixels to build a primitive non-saliency dictionary. We then prune the outliers in the dictionary and test on the remaining superpixels to iteratively refine the dictionary. Comprehensive experiments show that our approach universally outperforms the state-of-the-art solution on all 2D, 3D and 4D data.

@InProceedings{Li_2015_CVPR,author = {Li, Nianyi and Sun, Bilin and Yu, Jingyi},title = {A Weighted Sparse Coding Framework for Saliency Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu

Abstract: Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.

@InProceedings{Ma_2015_CVPR,author = {Ma, Ziyang and Liao, Renjie and Tao, Xin and Xu, Li and Jia, Jiaya and Wu, Enhua},title = {Handling Motion Blur in Multi-Frame Super-Resolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: We introduce RIANN (Ring Intersection Approximate Nearest Neighbor search), an algorithm for matching patches of a video to a set of reference patches in real-time. For each query, RIANN finds potential matches by intersecting rings around key points in appearance space. Its search complexity is reversely correlated to the amount of temporal change, making it a good fit for videos, where typically most patches change slowly with time. Experiments show that RIANN is up to two orders of magnitude faster than previous ANN methods, and is the only solution that operates in real-time. We further demonstrate how RIANN can be used for real-time video processing and provide examples for a range of real-time video applications, including colorization, denoising, and several artistic effects.

@InProceedings{Ben-Zrihem_2015_CVPR,author = {Ben-Zrihem, Nir and Zelnik-Manor, Lihi},title = {Approximate Nearest Neighbor Fields in Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: This paper proposes a robust multi-image based blind face hallucination framework to super-resolve LR faces. The proposed framework first estimates both blurring kernel and transformations of multiple LR faces by robust deblurring and registration in PCA subspace. A patch-wise mixture of probabilistic PCA prior is then incorporated for face super-resolution. Previous work on face SR using PCA prior can be viewed as special cases of the framework. Experimental results in both simulated and real LR sequences demonstrate very promising performance of the proposed method.

@InProceedings{Jin_2015_CVPR,author = {Jin, Yonggang and Bouganis, Christos-Savvas},title = {Robust Multi-Image Based Blind Face Hallucination},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Roee Litman, Simon Korman, Alexander Bronstein, Shai Avidan

Abstract: This work presents a novel approach for detecting inliers in a given set of correspondences (matches). It does so without explicitly identifying any consensus set, based on a method for inlier rate estimation (IRE). Given such an estimator for the inlier rate, we also present an algorithm that detects a globally optimal transformation. We provide a theoretical analysis of the IRE method using a stochastic generative model on the continuous spaces of matches and transformations. This model allows rigorous investigation of the limits of our IRE method for the case of 2D-translation, further giving bounds and insights for the more general case. Our theoretical analysis is validated empirically and is shown to hold in practice for the more general case of 2D-affinities. In addition, we show that the combined framework works on challenging cases of 2D-homography estimation, with very few and possibly noisy inliers, where RANSAC generally fails.

@InProceedings{Litman_2015_CVPR,author = {Litman, Roee and Korman, Simon and Bronstein, Alexander and Avidan, Shai},title = {Inverting RANSAC: Global Model Detection via Inlier Rate Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: For several decades, image restoration remains an active research topic in low-level computer vision and hence new approaches are constantly emerging. However, many recently proposed algorithms achieve state-of-the-art performance only at the expense of very high computation time, which clearly limits their practical relevance. In this work, we propose an effective approach with both high computational efficiency and high restoration quality. We extend conventional nonlinear reaction diffusion models by several parametrized linear filters as well as several parametrized influence functions.  We propose to train the parameters of the filters and the influence functions through a loss based approach. Experiments show that our trained nonlinear reaction diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for image restoration. Due to their structural simplicity, our trained models are highly efficient and are also well-suited for parallel computation on GPUs.

@InProceedings{Chen_2015_CVPR,author = {Chen, Yunjin and Yu, Wei and Pock, Thomas},title = {On Learning Optimized Reaction Diffusion Processes for Effective Image Restoration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Quynh Nguyen, Antoine Gautier, Matthias Hein

Abstract: The estimation of correspondences between two images resp. point sets is a core problem in computer vision. One way to formulate the problem is graph matching leading to the quadratic assignment problem which is NP-hard. Several so called second order methods have been proposed to solve this problem. In recent years hypergraph matching leading to a third order problem became popular as it allows for better integration of geometric information. For most of these third order algorithms no theoretical guarantees are known. In this paper we propose a general framework for tensor block coordinate ascent methods for hypergraph matching. We propose two algorithms which both come along with the guarantee of monotonic ascent in the matching score on the set of discrete assignment matrices. In the experiments we show that our new algorithms outperform previous work both in terms of achieving better matching scores and matching accuracy. This holds in particular for very challenging settings where one has a high number of outliers and other forms of noise.

@InProceedings{Nguyen_2015_CVPR,author = {Nguyen, Quynh and Gautier, Antoine and Hein, Matthias},title = {A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yannick Verdie, Kwang Yi, Pascal Fua, Vincent Lepetit

Abstract: We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in multiple training images taken from the same viewpoint. We then train a regressor to predict a score map whose maxima are those points so that they can be found by simple non-maximum suppression.  As there are no standard datasets to test the influence of these kinds of changes, we created our own, which we will make publicly available. We will show that our method significantly outperforms the state-of-the-art methods in such challenging conditions, while still achieving state-of-the-art performance on the untrained standard Oxford dataset.

@InProceedings{Verdie_2015_CVPR,author = {Verdie, Yannick and Yi, Kwang and Fua, Pascal and Lepetit, Vincent},title = {TILDE: A Temporally Invariant Learned DEtector},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xinlei Chen, Alan Ritter, Abhinav Gupta, Tom Mitchell

Abstract: We present a co-clustering framework that can be used to discover multiple semantic and visual senses of a given Noun Phrase (NP). Unlike traditional clustering approaches which assume a one-to-one mapping between the clusters in the text-based feature space and the visual space, we adopt a one-to-many mapping between the two spaces. This is primarily because each semantic sense (concept) can correspond to different visual senses due to viewpoint and appearance variations. Our structure-EM style optimization not only extracts the multiple senses in both semantic and visual feature space, but also discovers the mapping between the senses. We introduce a challenging dataset (CMU Polysemy-30) for this problem consisting of 30 NPs ($\sim$5600 labeled instances out of $\sim$22K total instances). We have also conducted a large-scale experiment that performs sense disambiguation for $\sim$2000 NPs.

@InProceedings{Chen_2015_CVPR,author = {Chen, Xinlei and Ritter, Alan and Gupta, Abhinav and Mitchell, Tom},title = {Sense Discovery via Co-Clustering on Images and Text},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Dihong Gong, Zhifeng Li, Dacheng Tao, Jianzhuang Liu, Xuelong Li

Abstract: In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition. First, a new maximum entropy feature descriptor (MEFD) is developed that encodes the microstructure of facial images into a set of discrete codes in terms of maximum entropy. By densely sampling the encoded face image, sufficient discriminatory and expressive information can be extracted for further analysis. A new matching method is also developed, called identity factor analysis (IFA), to estimate the probability that two faces have the same underlying identity. The effectiveness of the framework is confirmed by extensive experimentation on two face aging datasets, MORPH (the largest public-domain face aging dataset) and FGNET. We also conduct experiments on the famous LFW dataset to demonstrate the excellent generalizability of our new approach.

@InProceedings{Gong_2015_CVPR,author = {Gong, Dihong and Li, Zhifeng and Tao, Dacheng and Liu, Jianzhuang and Li, Xuelong},title = {A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Zicheng Liao, Kevin Karsch, David Forsyth

Abstract: We propose an approximate shading model for image-based object modeling and insertion. Our approach is a hybrid of 3D rendering and image-based composition. It avoids the difficulties of physically accurate shape estimation from a single image, and allows for more flexible image composition than pure image-based methods. The model decomposes the shading field into (a) a rough shape term that can be reshaded, (b) a parametric shading detail that encodes missing features from the first term, and (c) a geometric detail term that captures fine-scale material properties.  With this object model, we build an object relighting system that allows an artist to select an object from an image and insert it into a 3D scene. Through simple interactions, the system can adjust illumination on the inserted object so that it appears more naturally in the scene. Our quantitative evaluation and extensive user study suggest our method is a promising alternative to existing methods of object insertion.

@InProceedings{Liao_2015_CVPR,author = {Liao, Zicheng and Karsch, Kevin and Forsyth, David},title = {An Approximate Shading Model for Object Relighting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Qiang Chen, Junshi Huang, Rogerio Feris, Lisa M. Brown, Jian Dong, Shuicheng Yan

Abstract: We address the problem of describing people based on fine-grained clothing attributes. This is an important problem for many practical applications, such as identifying target suspects or finding missing people based on detailed clothing descriptions in surveillance videos or consumer photos. We approach this problem by first mining clothing images with fine-grained attribute labels from online shopping stores. A large-scale dataset is built with about one million images and fine-detailed attribute sub-categories, such as various shades of color (e.g., watermelon red, rosy red, purplish red), clothing types (e.g., down jacket, denim jacket), and patterns (e.g., thin horizontal stripes, houndstooth). As these images are taken in ideal pose/lighting/background conditions, it is unreliable to directly use them as training data for attribute prediction in the domain of unconstrained images captured, for example, by mobile phones or surveillance cameras. In order to bridge this gap, we propose a novel double-path deep domain adaptation network to model the data from the two domains jointly. Several alignment cost layers placed in-between the two columns ensure the consistency of the two domain features and the feasibility to predict unseen attribute categories in one of the domains. Finally, to achieve a working system with automatic human body alignment, we trained an enhanced RCNN-based detector to localize human bodies in images. Our extensive experimental evaluation demonstrates the effectiveness of the proposed approach for describing people based on fine-grained clothing attributes.

@InProceedings{Chen_2015_CVPR,author = {Chen, Qiang and Huang, Junshi and Feris, Rogerio and Brown, Lisa M. and Dong, Jian and Yan, Shuicheng},title = {Deep Domain Adaptation for Describing People Based on Fine-Grained Clothing Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Haoxiang Li, Zhe Lin, Xiaohui Shen, Jonathan Brandt, Gang Hua

Abstract: In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds.  Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade.  The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage.  The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.

@InProceedings{Li_2015_CVPR,author = {Li, Haoxiang and Lin, Zhe and Shen, Xiaohui and Brandt, Jonathan and Hua, Gang},title = {A Convolutional Neural Network Cascade for Face Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, Jianguo Zhang

Abstract: In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition. Considering that features from different channels could share some similar hidden structures, we propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogenous multi-task learning. The proposed model in an unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to enable the multi-task classifier learning, and 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by two inference models. Extensive results on three activity datasets have demonstrated the efficacy of the proposed method. In addition, a novel RGB-D activity dataset focusing on human-object interaction is collected for evaluating the proposed method, which will be made available to the community for RGB-D activity benchmarking and analysis.

@InProceedings{Hu_2015_CVPR,author = {Hu, Jian-Fang and Zheng, Wei-Shi and Lai, Jianhuang and Zhang, Jianguo},title = {Jointly Learning Heterogeneous Features for RGB-D Activity Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Fredo Durand, William T. Freeman

Abstract: The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motion in video. Objects tend to vibrate in a set of preferred modes. The shapes and frequencies of these modes depend on the structure and material properties of an object. Focusing on the case where geometry is known or fixed, we show how information about an object's modes of vibration can be extracted from video and used to make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of rods and fabrics by passively observing their motion in high-speed and regular framerate video.

@InProceedings{Davis_2015_CVPR,author = {Davis, Abe and Bouman, Katherine L. and Chen, Justin G. and Rubinstein, Michael and Durand, Fredo and Freeman, William T.},title = {Visual Vibrometry: Estimating Material Properties From Small Motion in Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Though recent advanced convolutional neural networks (CNNs) have been improving the image recognition accuracy, the models are getting more complex and time-consuming. For real-world applications in industrial and commercial scenarios, engineers and developers are often faced with the requirement of constrained time budget. In this paper, we investigate the accuracy of CNNs under constrained time cost. Under this constraint, the designs of the network architectures should exhibit as trade-offs among the factors like depth, numbers of filters, filter sizes, etc. With a series of controlled comparisons, we progressively modify a baseline model while preserving its time complexity. This is also helpful for understanding the importance of the factors in network designs. We present an architecture that achieves very competitive accuracy in the ImageNet dataset (11.8% top-5 error, 10-view test), yet is 20% faster than ``AlexNet'' (16.0% top-5 error, 10-view test).

@InProceedings{He_2015_CVPR,author = {He, Kaiming and Sun, Jian},title = {Convolutional Neural Networks at Constrained Time Cost},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Xiaofan Zhang, Hai Su, Lin Yang, Shaoting Zhang

Abstract: Computer-aided diagnosis of medical images requires thorough analysis of image details. For example, examining all cells enables fine-grained categorization of histopathological images. Traditional computational methods may have efficiency issues when performing such detailed analysis. In this paper, we propose a robust and scalable solution to achieve this. Specifically, a robust segmentation method is developed to delineate region-of-interests (e.g., cells) accurately, using hierarchical voting and repulsive active contour. A hashing-based large-scale retrieval approach is also designed to examine and classify them by comparing with a massive training database. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and the squamous carcinoma), using thousands of histopathological images extracted from hundreds of patients. Our method has achieved promising performance, i.e., 87.3% accuracy and 1.68 seconds by searching among half-million cells.

@InProceedings{Zhang_2015_CVPR,author = {Zhang, Xiaofan and Su, Hai and Yang, Lin and Zhang, Shaoting},title = {Fine-Grained Histopathological Image Analysis via Robust Segmentation and Large-Scale Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Total Variation (TV) is an effective and popular prior model in the field of regularization-  image processing. This paper focuses on TV for image restoration  in the presence of impulse noise. This type of noise frequently arises in data acquisition and  transmission due to many reasons, e.g. a faulty sensor or analog-to-digital converter errors. Removing this noise  is an important task in image restoration. State-of-the-art methods such as Adaptive Outlier Pursuit(AOP) [42], which is based on TV with L02-norm data fidelity, only give sub-optimal performance. In this paper, we propose a new method, called L0TV-PADMM, which solves the TV-based restoration problem with L0-norm data fidelity. To effectively deal with the resulting non-convex non-smooth optimization problem, we first reformulate it as an equivalent MPEC (Mathematical Program with Equilibrium Constraints), and then solve it using a proximal Alternating Direction Method of Multipliers (PADMM). Our L0TV-PADMM method finds a desirable solution to the original L0-norm optimization problem and is proven to be convergent under mild conditions. We apply L0TV-PADMM to the problems of image denoising and deblurring in the presence of impulse noise. Our extensive experiments demonstrate that L0TV-PADMM outperforms state-of-the-art image restoration methods.

@InProceedings{Yuan_2015_CVPR,author = {Yuan, Ganzhao and Ghanem, Bernard},title = {L0TV: A New Method for Image Restoration in the Presence of Impulse Noise},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Basura Fernando, Efstratios Gavves, Jose Oramas M., Amir Ghodrati, Tinne Tuytelaars

Abstract: In this paper we present a method to capture video-wide temporal information for action recognition. We postulate that a function capable of ordering the frames of a video temporally (based on the appearance) captures well the evolution of the appearance within the video. We learn such ranking functions per video via a ranking machine and use the parameters of these as a new video representation. The proposed method is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions.  We perform a large number of evaluations on datasets for generic action recognition (Hollywood2 and HMDB51), fine-grained actions (MPII- cooking activities) and gestures (Chalearn). Results show that the proposed method  brings an absolute improvement of 7-10\%, while being compatible with and complementary to further improvements in appearance and local motion based methods.

@InProceedings{Fernando_2015_CVPR,author = {Fernando, Basura and Gavves, Efstratios and Oramas, Jose M. and Ghodrati, Amir and Tuytelaars, Tinne},title = {Modeling Video Evolution for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Long-Term Correlation Tracking

Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming-Hsuan Yang

Abstract: In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-the-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn the discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness.

@InProceedings{Ma_2015_CVPR,author = {Ma, Chao and Yang, Xiaokang and Zhang, Chongyang and Yang, Ming-Hsuan},title = {Long-Term Correlation Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Anton Milan, Laura Leal-Taixe, Konrad Schindler, Ian Reid

Abstract: Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these "dots" over time. An obvious shortcoming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain an video segmentation in addition to the classical bounding-box representation in unconstrained, real-world sequences. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.

@InProceedings{Milan_2015_CVPR,author = {Milan, Anton and Leal-Taixe, Laura and Schindler, Konrad and Reid, Ian},title = {Joint Tracking and Segmentation of Multiple Targets},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Roy Or - El, Guy Rosman, Aaron Wetzler, Ron Kimmel, Alfred M. Bruckstein

Abstract: The popularity of low-cost RGB-D scanners is increasing on a daily basis. Nevertheless, existing scanners often cannot capture subtle details in the environment. We present a novel method to enhance the depth map by fusing the intensity and depth information to create more detailed range profiles. The lighting model we use can handle natural scene illumination. It is integrated in a shape from shading like technique to improve the visual fidelity of the reconstructed object. Unlike previous efforts in this domain, the detailed geometry is calculated directly, without the need to explicitly find and integrate surface normals. In addition, the proposed method operates four orders of magnitude faster than the state of the art. Qualitative and quantitative visual and statistical evidence support the improvement in the depth obtained by the suggested method.

@InProceedings{El_2015_CVPR,author = {Or - El, Roy and Rosman, Guy and Wetzler, Aaron and Kimmel, Ron and Bruckstein, Alfred M.},title = {RGBD-Fusion: Real-Time High Precision Depth Recovery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Yu Zhu, Yanning Zhang, Boyan Bonev, Alan L. Yuille

Abstract: We propose a single-image super-resolution method based on the gradient reconstruction. To predict the gradient field, we collect a dictionary of gradient patterns from an external set of images. We observe that there are patches representing singular primitive structures (e.g. a single edge), and non-singular ones (e.g. a triplet of edges). Based on the fact that singular primitive patches are more invariant to the scale change (i.e. have less ambiguity across different scales), we represent the non-singular primitives as compositions of singular ones, each of which is allowed some deformation. Both the input patches and dictionary elements are decomposed to contain only singular primitives. The compositional aspect of the model makes the gradient field more reliable. The deformable aspect makes the dictionary more expressive. As shown in our experimental results, the proposed method outperforms the state-of-the-art methods.

@InProceedings{Zhu_2015_CVPR,author = {Zhu, Yu and Zhang, Yanning and Bonev, Boyan and Yuille, Alan L.},title = {Modeling Deformable Gradient Compositions for Single-Image Super-Resolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Several state-of-the-art video deblurring methods are based on a strong assumption that the captured scenes are static. These methods fail to deblur blurry videos in dynamic scenes. We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, contrary to other methods. To handle locally varying and general blurs caused by various sources, such as camera shake, moving objects, and depth variation in a scene, we approximate pixel-wise kernel with bidirectional optical flows. Therefore, we propose a single energy model that simultaneously estimates optical flows and latent frames to solve our deblurring problem. We also provide a framework and efficient solvers to optimize the energy model. By minimizing the proposed energy function, we achieve significant improvements in removing blurs and estimating accurate optical flows in blurry frames. Extensive experimental results demonstrate the superiority of the proposed method in real and challenging videos that state-of-the-art methods fail in either deblurring or optical flow estimation.

@InProceedings{Kim_2015_CVPR,author = {Hyun Kim, Tae and Mu Lee, Kyoung},title = {Generalized Video Deblurring for Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Active Pictorial Structures

Epameinondas Antonakos, Joan Alabort-i-Medina, Stefanos Zafeiriou

Abstract: In this paper we present a novel generative deformable model motivated by Pictorial Structures (PS) and Active Appearance Models (AAMs) for object alignment in-the-wild. Inspired by the tree structure used in PS, the proposed Active Pictorial Structures (APS) model the appearance of the object using multiple graph-based pairwise normal distributions (Gaussian Markov Random Field) between the patches extracted from the regions around adjacent landmarks. We show that this formulation is more accurate than using a single multivariate distribution (Principal Component Analysis) as commonly done in the literature. APS employ a weighted inverse compositional Gauss-Newton optimization with fixed Jacobian and Hessian that achieves close to real-time performance and state-of-the-art results. Finally, APS have a spring-like graph-based deformation prior term that makes them robust to bad initializations. We present extensive experiments on the task of face alignment, showing that APS outperform current state-of-the-art methods. To the best of our knowledge, the proposed method is the first weighted inverse compositional technique that proves to be so accurate and efficient at the same time.

@InProceedings{Antonakos_2015_CVPR,author = {Antonakos, Epameinondas and Alabort-i-Medina, Joan and Zafeiriou, Stefanos},title = {Active Pictorial Structures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Ego-Surfing First-Person Videos

Ryo Yonetani, Kris M. Kitani, Yoichi Sato

Abstract: We envision a future time when wearable cameras (e.g., small cameras in glasses or pinned on a shirt collar) are worn by the masses and record first-person point-of-view (POV) videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera -- with or without consent. Motivated by these benefits and risks, we develop a self-search technique tailored to first-person POV videos. The key observation of our work is that the egocentric head motions of a target person (i.e., the self) are observed both in the POV video of the target and observer. The motion correlation between the target person's video and the observer's video can then be used to uniquely identify instances of the self. We incorporate this feature into our proposed approach that computes the motion correlation over supervoxel hierarchies to localize target instances in observer videos. Our proposed approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, automated video collection and social group discovery.

@InProceedings{Yonetani_2015_CVPR,author = {Yonetani, Ryo and Kitani, Kris M. and Sato, Yoichi},title = {Ego-Surfing First-Person Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks(CNN), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for extracting features at three different scales. Our learned saliency model is capable of achieving state-of-the-art performance on all public benchmarks. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, we point out that aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single region decomposition. To promote further research and evaluation of visual saliency models, we also construct a large database of 4447 challenging images and their pixelwise saliency annotation. Experimental results demonstrate that our proposed method significantly outperforms all existing saliency estimation techniques, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset, and lowering the mean absolute error by 5.7% and 35.1% respectively on the same two datasets.

@InProceedings{Li_2015_CVPR,author = {Li, Guanbin and Yu, Yizhou},title = {Visual Saliency Based on Multiscale Deep Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Kenichiro Tanaka, Yasuhiro Mukaigawa, Hiroyuki Kubo, Yasuyuki Matsushita, Yasushi Yagi

Abstract: This paper describes a method for recovering appearance of inner slices of translucent objects. The outer appearance of translucent objects is a summation of the appearance of slices at all depths, where each slice is blurred by depth-dependent point spread functions (PSFs). By exploiting the difference of low-pass characteristics of depth-dependent PSFs, we develop a multi-frequency illumination method for obtaining the appearance of individual inner slices using a coaxial projector-camera setup. Specifically, by measuring the target object with varying the spatial frequency of checker patterns emitted from a projector, our method recovers inner slices via a simple linear solution method. We quantitatively evaluate accuracy of the proposed method by simulations and show qualitative recovery results using real-world scenes.

@InProceedings{Tanaka_2015_CVPR,author = {Tanaka, Kenichiro and Mukaigawa, Yasuhiro and Kubo, Hiroyuki and Matsushita, Yasuyuki and Yagi, Yasushi},title = {Recovering Inner Slices of Translucent Objects by Multi-Frequency Illumination},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


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

Abstract: The common graph Laplacian regularizer is well-established in semi-supervised learning and spectral dimensionality reduction. However, as a first-order regularizer, it can lead to degenerate functions in high-dimensional manifolds. The iterated graph Laplacian enables high-order regularization, but it has a high computational complexity and so cannot be applied to large problems. We introduce a new regularizer which is globally high order and so does not suffer from the degeneracy of the graph Laplacian regularizer, but is also sparse for efficient computation in semi-supervised learning applications. We reduce computational complexity by building a local first-order approximation of the manifold as a surrogate geometry, and construct our high-order regularizer based on local derivative evaluations therein. Experiments on human body shape and pose analysis demonstrate the effectiveness and efficiency of our method.

@InProceedings{Kim_2015_CVPR,author = {In Kim, Kwang and Tompkin, James and Pfister, Hanspeter and Theobalt, Christian},title = {Local High-Order Regularization on Data Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Abstract: A video dataset that is designed to study fine-grained categorisation of pedestrians is introduced. Pedestrians were recorded ``in-the-wild'' from a moving vehicle. Annotations include bounding boxes, tracks, 14 keypoints with occlusion information and the fine-grained categories of age (5 classes), sex (2 classes), weight (3 classes) and clothing style (4 classes). There are a total of 27,454 bounding box and pose labels across 4222 tracks. This dataset is designed to train and test algorithms for fine-grained categorisation of people; it is also useful for benchmarking tracking, detection and pose estimation of pedestrians. State-of-the-art algorithms for fine-grained classification and pose estimation were tested using the dataset and the results are reported as a useful performance baseline.

@InProceedings{Hall_2015_CVPR,author = {Hall, David and Perona, Pietro},title = {Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Curriculum Learning of Multiple Tasks

Anastasia Pentina, Viktoriia Sharmanska, Christoph H. Lampert

Abstract: Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between the most related tasks. In this work we propose an approach that processes multiple tasks in a sequence with sharing between subsequent tasks instead of solving all tasks jointly. Subsequently, we address the question of curriculum learning of tasks, i.e. finding the best order of tasks to be learned. Our approach is based on a generalization bound criterion for choosing the task order that optimizes the average expected classification performance over all tasks. Our experimental results show that learning multiple related tasks sequentially can be more effective than learning them jointly, the order in which tasks are being solved affects the overall performance, and that our model is able to automatically discover a favourable order of tasks.

@InProceedings{Pentina_2015_CVPR,author = {Pentina, Anastasia and Sharmanska, Viktoriia and Lampert, Christoph H.},title = {Curriculum Learning of Multiple Tasks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Sayed Hossein Khatoonabadi, Nuno Vasconcelos, Ivan V. Bajic, Yufeng Shan

Abstract: Visual saliency has been shown to depend on the unpredictability of the visual stimulus given its surround. Various previous works have advocated the equivalence between stimulus saliency and uncompressibility. We propose a direct measure of this quantity, namely the number of bits required by an optimal video compressor to encode a given video patch, and show that features derived from this measure are highly predictive of eye fixations. To account for global saliency effects, these are embedded in a Markov random field model. The resulting saliency measure is shown to achieve state-of-the-art accuracy for the prediction of fixations, at a very low computational cost. Since most modern cameras incorporate video encoders, this paves the way for in-camera saliency estimation, which could be useful in a variety of computer vision applications.

@InProceedings{Khatoonabadi_2015_CVPR,author = {Hossein Khatoonabadi, Sayed and Vasconcelos, Nuno and Bajic, Ivan V. and Shan, Yufeng},title = {How Many Bits Does it Take for a Stimulus to Be Salient?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Nikolay Savinov, Lubor Ladicky, Christian Hane, Marc Pollefeys

Abstract: Dense semantic 3D reconstruction is typically formulated as a discrete or continuous problem over label assignments in a voxel grid, combining semantic and depth likelihoods in a Markov Random Field framework. The depth and semantic information is incorporated as a unary potential, smoothed by a pairwise regularizer. However, modelling likelihoods as a unary potential does not model the problem correctly leading to various undesirable visibility artifacts.  We propose to formulate an optimization problem that directly optimizes the reprojection error of the 3D model with respect to the image estimates, which corresponds to the optimization over rays, where the cost function depends on the semantic class and depth of the first occupied voxel along the ray. The 2-label formulation is made feasible by transforming it into a graph-representable form under QPBO relaxation, solvable using graph cut. The multi-label problem is solved by applying $\alpha$-expansion using the same relaxation in each expansion move. Our method was indeed shown to be feasible in practice, running comparably fast to the competing methods, while not suffering from ray potential approximation artifacts.

@InProceedings{Savinov_2015_CVPR,author = {Savinov, Nikolay and Ladicky, Lubor and Hane, Christian and Pollefeys, Marc},title = {Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


On the Appearance of Translucent Edges

Ioannis Gkioulekas, Bruce Walter, Edward H. Adelson, Kavita Bala, Todd Zickler

Abstract: Edges in images of translucent objects are very different from edges in images of opaque objects. The physical causes for these differences are hard to characterize analytically and are not well understood. This paper considers one class of translucency edges---those caused by a discontinuity in surface orientation---and describes the physical causes of their appearance. We simulate thousands of translucency edge profiles using many different scattering material parameters, and we explain the resulting variety of edge patterns by qualitatively analyzing light transport. We also discuss the existence of shape and material metamers, or combinations of distinct shape or material parameters that generate the same edge profile. This knowledge is relevant to visual inference tasks that involve translucent objects, such as shape or material estimation.

@InProceedings{Gkioulekas_2015_CVPR,author = {Gkioulekas, Ioannis and Walter, Bruce and Adelson, Edward H. and Bala, Kavita and Zickler, Todd},title = {On the Appearance of Translucent Edges},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Chenglong Li, Liang Lin, Wangmeng Zuo, Shuicheng Yan, Jin Tang

Abstract: This paper investigates how to perform robust and efficient unsupervised video segmentation while suppressing the effects of data noises and/or corruptions. We propose a general algorithm, called Sub-Optimal Low-rank Decomposition (SOLD), which pursues the low-rank representation for video segmentation. Given the supervoxels affinity matrix of an observed video sequence, SOLD seeks a sub-optimal solution by making the matrix rank explicitly determined. In particular, the affinity matrix with the rank fixed can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions. Moreover, we incorporate a discriminative replication prior into our framework based on the obervation that small-size video patterns tend to recur frequently within the same object. The video can be segmented into several spatio-temporal regions by applying the Normalized-Cut (NCut) algorithm with the solved low-rank representation. To process the streaming videos, we apply our algorithm sequentially over a batch of frames over time, in which we also develop several temporal consistent constraints improving the robustness. Extensive experiments on the public benchmarks demonstrate superior performance of our framework over other state-of-the-art approaches.

@InProceedings{Li_2015_CVPR,author = {Li, Chenglong and Lin, Liang and Zuo, Wangmeng and Yan, Shuicheng and Tang, Jin},title = {SOLD: Sub-Optimal Low-rank Decomposition for Efficient Video Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Visesh Chari, Simon Lacoste-Julien, Ivan Laptev, Josef Sivic

Abstract: Multi-object tracking has been recently approached with the min-cost network flow optimization techniques. Such methods simultaneously resolve multiple object tracks in a video and enable modeling of dependencies among tracks. Min-cost network flow methods also fit well within the "tracking-by-detection" paradigm where object trajectories are obtained by connecting per-frame outputs of an object detector. Object detectors, however, often fail due to occlusions and clutter in the video. To cope with such situations, we propose to add pairwise costs to the min-cost network flow framework. While integer solutions to such a problem become NP-hard, we design a convex relaxation solution with an efficient rounding heuristic which empirically gives certificates of small suboptimality. We evaluate two particular types of pairwise costs and demonstrate improvements over recent tracking methods in real-world video sequences.

@InProceedings{Chari_2015_CVPR,author = {Chari, Visesh and Lacoste-Julien, Simon and Laptev, Ivan and Sivic, Josef},title = {On Pairwise Costs for Network Flow Multi-Object Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Jonathan Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei

Abstract: Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.

@InProceedings{Krause_2015_CVPR,author = {Krause, Jonathan and Jin, Hailin and Yang, Jianchao and Fei-Fei, Li},title = {Fine-Grained Recognition Without Part Annotations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}


Robust Reconstruction of Indoor Scenes

Sungjoon Choi, Qian-Yi Zhou, Vladlen Koltun

Abstract: We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geometric registration of scene fragments with robust global optimization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to aliasing of geometric detail and inability to disambiguate different surfaces in the scene. The presented optimization approach disables erroneous geometric alignments even when they significantly outnumber correct ones. Experimental results demonstrate that the presented approach substantially increases the accuracy of reconstructed scene models.

@InProceedings{Choi_2015_CVPR,author = {Choi, Sungjoon and Zhou, Qian-Yi and Koltun, Vladlen},title = {Robust Reconstruction of Indoor Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}}