Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang

Abstract: This paper presents a scalable scene parsing algorithm based on image retrieval and superpixel matching. We focus on rare object classes, which play an important role in achieving richer semantic understanding of visual scenes, compared to common background classes. Towards this end, we make two novel contributions: rare class expansion and semantic context description. First, considering the long-tailed nature of the label distribution, we expand the retrieval set by rare class exemplars and thus achieve more balanced superpixel classification results. Second, we incorporate both global and local semantic context information through a feedback based mechanism to refine image retrieval and superpixel matching. Results on the SIFTflow and LMSun datasets show the superior performance of our algorithm, especially on the rare classes, without sacrificing overall labeling accuracy.

@InProceedings{Yang_2014_CVPR,author = {Yang, Jimei and Price, Brian and Cohen, Scott and Yang, Ming-Hsuan},title = {Context Driven Scene Parsing with Attention to Rare Classes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jacob Walker, Abhinav Gupta, Martial Hebert

Abstract: In this paper we present a conceptually simple but surprisingly powerful method for visual prediction which combines the effectiveness of mid-level visual elements with temporal modeling. Our framework can be learned in a completely unsupervised manner from a large collection of videos. However, more importantly, because our approach models the prediction framework on these mid-level elements, we can not only predict the possible motion in the scene but also predict visual appearances — how are appearances going to change with time. This yields a visual "hallucination" of probable events on top of the scene. We show that our method is able to accurately predict and visualize simple future events; we also show that our approach is comparable to supervised methods for event prediction.

@InProceedings{Walker_2014_CVPR,author = {Walker, Jacob and Gupta, Abhinav and Hebert, Martial},title = {Patch to the Future: Unsupervised Visual Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We consider the design of a single vector representation for an image that embeds and aggregates a set of local patch descriptors such as SIFT. More specifically we aim to construct a dense representation, like the Fisher Vector or VLAD, though of small or intermediate size.  We make two contributions, both aimed at regularizing the individual contributions of the local descriptors in the final representation. The first is a novel embedding method that avoids the dependency on absolute distances by encoding directions. The second contribution is a "democratization" strategy that further limits the interaction of unrelated descriptors in the aggregation stage.  These methods are complementary and give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks.

@InProceedings{Jegou_2014_CVPR,author = {Jegou, Herve and Zisserman, Andrew},title = {Triangulation Embedding and Democratic Aggregation for Image Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Xin Yuan, Patrick Llull, Xuejun Liao, Jianbo Yang, David J. Brady, Guillermo Sapiro, Lawrence Carin

Abstract: A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover multiple color frames for each of the original measured frames, and each of the recovered frames can be focused at a different depth. The recovery of multiple frames for each measured frame is made possible via high-speed coding, manifested via translation of a single coded aperture; the inexpensive translation is constituted by mounting the binary code on a piezoelectric device. To simultaneously recover depth information, a liquid lens is modulated at high speed, via a variable voltage. Consequently, during the aforementioned coding process, the liquid lens allows the camera to sweep the focus through multiple depths. In addition to designing and implementing the camera, fast recovery is achieved by an anytime algorithm exploiting the group-sparsity of wavelet/DCT coefficients.

@InProceedings{Yuan_2014_CVPR,author = {Yuan, Xin and Llull, Patrick and Liao, Xuejun and Yang, Jianbo and Brady, David J. and Sapiro, Guillermo and Carin, Lawrence},title = {Low-Cost Compressive Sensing for Color Video and Depth},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Zhaolin Xiao, Qing Wang, Guoqing Zhou, Jingyi Yu

Abstract: When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts. Previous approaches have focused on avoiding aliasing by pre-processing the acquired light field via prefiltering, demosaicing, reparameterization, etc. In this paper, we present a different solution that first detects and then removes aliasing at the light field refocusing stage. Different from previous frequency domain aliasing analysis, we carry out a spatial domain analysis to reveal whether the aliasing would occur and uncover where in the image it would occur. The spatial analysis also facilitates easy separation of the aliasing vs. non-aliasing regions and aliasing removal. Experiments on both synthetic scene and real light field camera array data sets demonstrate that our approach has a number of advantages over the classical prefiltering and depth-dependent light field rendering techniques.

@InProceedings{Xiao_2014_CVPR,author = {Xiao, Zhaolin and Wang, Qing and Zhou, Guoqing and Yu, Jingyi},title = {Aliasing Detection and Reduction in Plenoptic Imaging},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Illumination-Aware Age Progression

Ira Kemelmacher-Shlizerman, Supasorn Suwajanakorn, Steven M. Seitz

Abstract: We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination. Leveraging thousands of photos of children and adults at many ages from the Internet, we first show how to compute average image subspaces that are pixel-to-pixel aligned and model variable lighting.  These averages depict a prototype man and woman aging from 0 to 80, under any desired illumination,  and capture the differences in shape and texture between ages.  Applying these differences to a new photo yields an age progressed result.   Contributions include relightable age subspaces, a novel technique for subspace-to-subspace alignment, and  the most extensive evaluation of age progression techniques in the literature.

@InProceedings{Kemelmacher-Shlizerman_2014_CVPR,author = {Kemelmacher-Shlizerman, Ira and Suwajanakorn, Supasorn and Seitz, Steven M.},title = {Illumination-Aware Age Progression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Youngbae Hwang, Joon-Young Lee, In So Kweon, Seon Joo Kim

Abstract: This paper introduces a new color transfer method which is a process of transferring color of an image to match the color of another image of the same scene. The color of a scene may vary from image to image because the photographs are taken at different times, with different cameras, and under different camera settings. To solve for a full nonlinear and nonparametric color mapping in the 3D RGB color space,  we propose a scattered point interpolation scheme using moving least squares and strengthen it with a probabilistic modeling of the color transfer in the 3D color space to deal with mis-alignments and noise. Experiments show the effectiveness of our method over previous color transfer methods both quantitatively and qualitatively. In addition, our framework can be applied for various instances of color transfer such as transferring color between different camera models, camera settings, and illumination conditions, as well as for video color transfers.

@InProceedings{Hwang_2014_CVPR,author = {Hwang, Youngbae and Lee, Joon-Young and So Kweon, In and Joo Kim, Seon},title = {Color Transfer Using Probabilistic Moving Least Squares},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yu Ji, Jinwei Ye, Sing Bing Kang, Jingyi Yu

Abstract: The goal of image pre-compensation is to process an image such that after being convolved with a known kernel, will appear close to the sharp reference image. In a practical setting, the pre-compensated image has significantly higher dynamic range than the latent image. As a result, some form of tone mapping is needed. In this paper, we show how global tone mapping functions affect contrast and ringing in image pre-compensation. In particular, we show that linear tone mapping eliminates ringing but incurs severe contrast loss, while non-linear tone mapping functions such as Gamma curves slightly enhances contrast but introduces ringing. To enable quantitative analysis, we design new metrics to measure the contrast of an image with ringing. Specifically, we set out to find its "equivalent ringing-free" image that matches its intensity histogram and uses its contrast as the measure. We illustrate our approach on projector defocus compensation and visual acuity enhancement. Compared with the state-of-the-art, our approach significantly improves the contrast. We believe our technique is the first to analytically trade-off between contrast and ringing.

@InProceedings{Ji_2014_CVPR,author = {Ji, Yu and Ye, Jinwei and Bing Kang, Sing and Yu, Jingyi},title = {Image Pre-compensation: Balancing Contrast and Ringing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Time-Mapping Using Space-Time Saliency

Feng Zhou, Sing Bing Kang, Michael F. Cohen

Abstract: We describe a new approach for generating regular-speed, low-frame-rate (LFR) video from a high-frame-rate (HFR) input while preserving the important moments in the original. We call this time-mapping, a time-based analogy to high dynamic range to low dynamic range spatial tone-mapping. Our approach makes these contributions: (1) a robust space-time saliency method for evaluating visual importance, (2) a re-timing technique to temporally resample based on frame importance, and (3) temporal filters to enhance the rendering of salient motion. Results of our space-time saliency method on a benchmark dataset show it is state-of-the-art. In addition, the benefits of our approach to HFR-to-LFR time-mapping over more direct methods are demonstrated in a user study.

@InProceedings{Zhou_2014_CVPR,author = {Zhou, Feng and Bing Kang, Sing and Cohen, Michael F.},title = {Time-Mapping Using Space-Time Saliency},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Image deblurring to remove blur caused by camera shake has been intensively studied. Nevertheless, most methods are brittle and computationally expensive. In this paper we analyze multi-image approaches, which capture and combine multiple frames in order to make deblurring more robust and tractable. In particular, we compare the performance of two approaches: align-and-average and multi-image deconvolution. Our deconvolution is non-blind, using a blur model obtained from real camera motion as measured by a gyroscope. We show that in most situations such deconvolution outperforms align-and-average. We also show, perhaps surprisingly, that deconvolution does not benefit from increasing exposure time beyond a certain threshold. To demonstrate the effectiveness and efficiency of our method, we apply it to still-resolution imagery of natural scenes captured using a mobile camera with flexible camera control and an attached gyroscope.

@InProceedings{Park_2014_CVPR,author = {Hee Park, Sung and Levoy, Marc},title = {Gyro-Based Multi-Image Deconvolution for Removing Handshake Blur},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jing Li, Zhichao Lu, Gang Zeng, Rui Gan, Hongbin Zha

Abstract: This paper describes a patchwork assembly algorithm for depth image super-resolution. An input low resolution depth image is disassembled into parts by matching similar regions on a set of high resolution training images, and a super-resolution image is then assembled using these corresponding matched counterparts. We convert the super resolution problem into a Markov Random Field (MRF) labeling problem, and propose a unified formulation embedding (1) the consistency between the resolution enhanced image and the original input, (2) the similarity of disassembled parts with the corresponding regions on training images, (3) the depth smoothness in local neighborhoods, (4) the additional geometric constraints from self-similar structures in the scene, and (5) the boundary coincidence between the resolution enhanced depth image and an optional aligned high resolution intensity image. Experimental results on both synthetic and real-world data demonstrate that the proposed algorithm is capable of recovering high quality depth images with X4 resolution enhancement along each coordinate direction, and that it outperforms state-of-the-arts [14] in both qualitative and quantitative evaluations.

@InProceedings{Li_2014_CVPR,author = {Li, Jing and Lu, Zhichao and Zeng, Gang and Gan, Rui and Zha, Hongbin},title = {Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Zhe Hu, Sunghyun Cho, Jue Wang, Ming-Hsuan Yang

Abstract: Images taken in low-light conditions with handheld cameras are often blurry due to the required long exposure time. Although significant progress has been made recently on image deblurring, state-of-the-art approaches often fail on low-light images, as these images do not contain a sufficient number of salient features that deblurring methods rely on. On the other hand, light streaks are common phenomena in low-light images that contain rich blur information, but have not been extensively explored in previous approaches. In this work, we propose a new method that utilizes light streaks to help deblur low-light images. We introduce a non-linear blur model that explicitly models light streaks and their underlying light sources, and poses them as constraints for estimating the blur kernel in an optimization framework. Our method also automatically detects useful light streaks in the input image. Experimental results show that our approach obtains good results on challenging real-world examples that no other methods could achieve before.

@InProceedings{Hu_2014_CVPR,author = {Hu, Zhe and Cho, Sunghyun and Wang, Jue and Yang, Ming-Hsuan},title = {Deblurring Low-light Images with Light Streaks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Depth captured by consumer RGB-D cameras is often noisy and misses values at some pixels, especially around object boundaries. Most existing methods complete the missing depth values guided by the corresponding color image. When the color image is noisy or the correlation between color and depth is weak, the depth map cannot be properly enhanced. In this paper, we present a depth map enhancement algorithm that performs depth map completion and de-noising simultaneously. Our method is based on the observation that similar RGB-D patches lie in a very low-dimensional subspace. We can then assemble the similar patches into a matrix and enforce this low-rank subspace constraint. This low-rank subspace constraint essentially captures the underlying structure in the RGB-D patches and enables robust depth enhancement against the noise or weak correlation between color and depth. Based on this subspace constraint, our method formulates depth map enhancement as a low-rank matrix completion problem. Since the rank of a matrix changes over matrices, we develop a data-driven method to automatically determine the rank number for each matrix. The experiments on both public benchmarks and our own captured RGB-D images show that our method can effectively enhance depth maps.

@InProceedings{Lu_2014_CVPR,author = {Lu, Si and Ren, Xiaofeng and Liu, Feng},title = {Depth Enhancement via Low-rank Matrix Completion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Rang Nguyen, Dilip K. Prasad, Michael S. Brown

Abstract: Camera images saved in raw format are being adopted in computer vision tasks since raw values represent minimally processed sensor responses.  Camera manufacturers, however, have yet to adopt a standard for raw images and current raw-rgb values are device specific due to different sensors spectral sensitivities.   This results in significantly different raw images for the same scene captured with different cameras.  This paper focuses on estimating a mapping that can convert a raw image of an arbitrary scene and illumination from one camera's raw space to another.  To this end, we examine various mapping strategies including linear and non-linear transformations applied both in a global and illumination-specific manner.  We show that illumination-specific mappings give the best result, however, at the expense of requiring a large number of transformations.  To address this issue, we introduce an illumination-independent mapping approach that uses white-balancing to assist in reducing the number of required transformations.  We show that this approach achieves state-of-the-art results on a range of consumer cameras and images of arbitrary scenes and illuminations.

@InProceedings{Nguyen_2014_CVPR,author = {Nguyen, Rang and Prasad, Dilip K. and Brown, Michael S.},title = {Raw-to-Raw: Mapping between Image Sensor Color Responses},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Establishing dense correspondences reliably between a pair of images is an important vision task with many applications. Though significant advance has been made towards estimating dense stereo and optical flow fields for two images adjacent in viewpoint or in time, building reliable dense correspondence fields for two general images still remains largely unsolved. For instance, two given images sharing some content exhibit dramatic photometric and geometric variations, or they depict different 3D scenes of similar scene characteristics. Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently. This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task. Inspired by the recent PatchMatch Filter technique, we leverage and extend a few established methods: 1) DAISY descriptors, 2) filter-based efficient flow inference, and 3) the PatchMatch fast search. Coupling and optimizing these modules seamlessly with image segments as the bridge, the proposed DFF approach enables efficiently performing dense descriptor-based correspondence field estimation in a generalized high-dimensional label space, which is augmented by scales and rotations. Experiments on a variety of challenging scenes show that our DFF approach estimates spatially coherent yet discontinuity-preserving image alignment results both robustly and efficiently.

@InProceedings{Yang_2014_CVPR,author = {Yang, Hongsheng and Lin, Wen-Yan and Lu, Jiangbo},title = {DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We introduce a method that can register challenging images from specular and poorly textured 3D environments, on which previous approaches  fail.  We assume that a small set of  reference images  of  the environment  and  a partial  3D  model are   available. Like  previous approaches, we  register the input images  by aligning them  with one  of  the  reference images  using  the  3D information.  However, these approaches typically  rely on the pixel intensities  for the alignment, which is prone to fail in presence of specularities or in absence of texture.  Our main contribution is an efficient novel local descriptor that we use to describe each image location.  We show that we can rely on this descriptor in place of the intensities to significantly improve the alignment robustness at a minor increase  of the  computational cost,  and we  analyze the reasons behind the success of our descriptor.

@InProceedings{Crivellaro_2014_CVPR,author = {Crivellaro, Alberto and Lepetit, Vincent},title = {Robust 3D Tracking with Descriptor Fields},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Iason Oikonomidis, Manolis I.A. Lourakis, Antonis A. Argyros

Abstract: We present a new method for tracking the 3D position, global orientation and full articulation of human hands. Following recent advances in model-based, hypothesize-and-test methods, the high-dimensional parameter space of hand configurations is explored with a novel evolutionary optimization technique specifically tailored to the problem. The proposed method capitalizes on the fact that samples from quasi-random sequences such as the Sobol have low discrepancy and exhibit a more uniform coverage of the sampled space compared to random samples obtained from the uniform distribution. The method has been tested for the problems of tracking the articulation of a single hand (27D parameter space) and two hands (54D space). Extensive experiments have been carried out with synthetic and real data, in comparison with state of the art methods. The quantitative evaluation shows that for cases of limited computational resources, the new approach achieves a speed-up of four (single hand tracking) and eight (two hands tracking) without compromising tracking accuracy. Interestingly, the proposed method is preferable compared to the state of the art either in the case of limited computational resources or in the case of more complex (i.e., higher dimensional) problems, thus improving the applicability of the method in a number of application domains.

@InProceedings{Oikonomidis_2014_CVPR,author = {Oikonomidis, Iason and Lourakis, Manolis I.A. and Argyros, Antonis A.},title = {Evolutionary Quasi-random Search for Hand Articulations Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We consider the problem of tracking multiple interacting objects in 3D, using RGBD input and by considering a hypothesize-and-test approach. Due to their interaction, objects to be tracked are expected to occlude each other in the field of view of the camera observing them. A naive approach would be to employ a Set of Independent Trackers (SIT) and to assign one tracker to each object. This approach scales well with the number of objects but fails as occlusions become stronger due to their disjoint consideration. The solution representing the current state of the art employs a single Joint Tracker (JT) that accounts for all objects simultaneously. This directly resolves ambiguities due to occlusions but has a computational complexity that grows geometrically with the number of tracked objects. We propose a middle ground, namely an Ensemble of Collaborative Trackers (ECT), that combines best traits from both worlds to deliver a practical and accurate solution to the multi-object 3D tracking problem. We present quantitative and qualitative experiments with several synthetic and real world sequences of diverse complexity. Experiments demonstrate that ECT manages to track far more complex scenes than JT at a computational time that is only slightly larger than that of SIT.

@InProceedings{Kyriazis_2014_CVPR,author = {Kyriazis, Nikolaos and Argyros, Antonis},title = {Scalable 3D Tracking of Multiple Interacting Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Bayesian Active Appearance Models

Joan Alabort-i-Medina, Stefanos Zafeiriou

Abstract: In this paper we provide the first, to the best of our knowledge, Bayesian formulation of one of the most successful and well-studied statistical models of shape and texture, i.e. Active Appearance Models (AAMs). To this end, we use a simple probabilistic model for texture generation assuming both Gaussian noise and a Gaussian prior over a latent texture space. We retrieve the shape parameters by formulating a novel cost function obtained by marginalizing out the latent texture space. This results in a fast implementation when compared to other simultaneous algorithms for fitting AAMs, mainly due to the removal of the calculation of texture parameters. We demonstrate that, contrary to what is believed regarding the performance of AAMs in generic fitting scenarios, optimization of the proposed cost function produces results that outperform discriminatively trained state-of-the-art methods in the problem of facial alignment "in the wild".

@InProceedings{Alabort-i-Medina_2014_CVPR,author = {Alabort-i-Medina, Joan and Zafeiriou, Stefanos},title = {Bayesian Active Appearance Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Chun-Hao Huang, Edmond Boyer, Nassir Navab, Slobodan Ilic

Abstract: This paper considers human tracking in multi-view setups and investigates a robust strategy that learns online key poses to drive a shape tracking method. The interest arises in realistic dynamic scenes where occlusions or segmentation errors occur. The corrupted observations present missing data and outliers that deteriorate tracking results. We propose to use key poses of the tracked person as multiple reference models. In contrast to many existing approaches that rely on a single reference model, multiple templates represent a larger variability of human poses. They provide therefore better initial hypotheses when tracking with noisy data. Our approach identifies these reference models online as distinctive keyframes during tracking. The most suitable one is then chosen as the reference at each frame. In addition, taking advantage of the proximity between successive frames, an efficient outlier handling technique is proposed to prevent from associating the model to irrelevant outliers. The two strategies are successfully experimented with a surface deformation framework that recovers both the pose and the shape. Evaluations on existing datasets also demonstrate their benefits with respect to the state of the art.

@InProceedings{Huang_2014_CVPR,author = {Huang, Chun-Hao and Boyer, Edmond and Navab, Nassir and Ilic, Slobodan},title = {Human Shape and Pose Tracking Using Keyframes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Bryan Poling, Gilad Lerman, Arthur Szlam

Abstract: Feature tracking in video is a crucial task in computer vision. Usually, the tracking problem is handled one feature at a time, using a single-feature tracker like the Kanade-Lucas-Tomasi algorithm, or one of its derivatives. While this approach works quite well when dealing with high-quality video and "strong" features, it often falters when faced with dark and noisy video containing low-quality features. We present a framework for jointly tracking a set of features, which enables sharing information between the different features in the scene. We show that our method can be employed to track features for both rigid and non-rigid motions (possibly of few moving bodies) even when some features are occluded. Furthermore, it can be used to significantly improve tracking results in poorly-lit scenes (where there is a mix of good and bad features). Our approach does not require direct modeling of the structure or the motion of the scene, and runs in real time on a single CPU core.

@InProceedings{Poling_2014_CVPR,author = {Poling, Bryan and Lerman, Gilad and Szlam, Arthur},title = {Better Feature Tracking Through Subspace Constraints},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: This paper presents a framework for simultaneously tracking, learning and parsing objects with a hierarchical and compositional And-Or graph (AOG) representation. The AOG is discriminatively learned online to account for the appearance (e.g., lighting and partial occlusion) and structural (e.g., different poses and viewpoints) variations of the object itself, as well as the distractors (e.g., similar objects) in the scene background. In tracking, the state of the object (i.e., bounding box) is inferred by parsing with the current AOG using a spatial-temporal dynamic programming (DP) algorithm. When the AOG grows big for handling objects with large variations in long-term tracking, we propose a bottom-up/top-down scheduling scheme for efficient inference, which performs focused inference with the most stable and discriminative small sub-AOG. During online learning, the AOG is re-learned iteratively with two steps: (i) Identifying the false positives and false negatives of the current AOG in a new frame by exploiting the spatial and temporal constraints observed in the trajectory; (ii) Updating the structure of the AOG, and re-estimating the parameters based on the augmented training dataset. In experiments, the proposed method outperforms state-of-theart tracking algorithms on a recent public tracking benchmark with 50 testing videos and 30 publicly available trackers evaluated [34].

@InProceedings{Lu_2014_CVPR,author = {Lu, Yang and Wu, Tianfu and Chun Zhu, Song},title = {Online Object Tracking, Learning and Parsing with And-Or Graphs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We present a video object segmentation approach that extends the particle filter to a region-based image representation. Image partition is  considered part of the particle filter measurement, which enriches the available information and leads to a re-formulation of the particle filter.  The prediction step uses a co-clustering between the previous image object partition and a partition of the current one, which allows us to tackle  the evolution of non-rigid structures. Particles are defined as unions of regions in the current image partition and their propagation is computed  through a single co-clustering. The proposed technique is assessed on the SegTrack dataset, leading to  satisfactory perceptual results and obtaining very competitive pixel error rates compared with the state-of-the-art methods.

@InProceedings{Varas_2014_CVPR,author = {Varas, David and Marques, Ferran},title = {Region-based Particle Filter for Video Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper, we present a novel online visual tracking method based on linear representation. First, we present a novel probability continuous outlier model (PCOM) to depict the continuous outliers that occur in the linear representation model. In the proposed model, the element of the noisy observation sample can be either represented by a PCA subspace with small Guassian noise or treated as an arbitrary value with a uniform prior, in which the spatial consistency prior is exploited by using a binary Markov  random field model. Then, we derive the objective function of the PCOM method, the solution of which can be iteratively obtained by the outlier-free least squares and standard max-flow/min-cut steps. Finally, based on the proposed PCOM method, we design an effective observation likelihood function and a simple update scheme for visual  tracking. Both qualitative and quantitative evaluations demonstrate that our tracker achieves very favorable performance in terms of both accuracy and speed.

@InProceedings{Wang_2014_CVPR,author = {Wang, Dong and Lu, Huchuan},title = {Visual Tracking via Probability Continuous Outlier Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


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

Abstract: A novel visual tracking algorithm using patch-based appearance models is proposed in this paper. We first divide the bounding box of a target object into multiple patches and then select only pertinent patches, which occur repeatedly near the center of the bounding box, to construct the foreground appearance model. We also divide the input image into non-overlapping blocks, construct a background model at each block location, and integrate these background models for tracking. Using the appearance models, we obtain an accurate foreground probability map. Finally, we estimate the optimal object position by maximizing the likelihood, which is obtained by convolving the foreground probability map with the pertinence mask. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art tracking algorithms significantly in terms of center position errors and success rates.

@InProceedings{Lee_2014_CVPR,author = {Lee, Dae-Youn and Sim, Jae-Young and Kim, Chang-Su},title = {Visual Tracking Using Pertinent Patch Selection and Masking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: This paper proposes a robust tracking method that uses interval analysis. Any single posterior model necessarily includes a modeling uncertainty (error), and thus, the posterior should be represented as an interval of probability. Then, the objective of visual tracking becomes to find the best state that maximizes the posterior and minimizes its interval simultaneously. By minimizing the interval of the posterior, our method can reduce the modeling uncertainty in the posterior. In this paper, the aforementioned objective is achieved by using the M4 estimation, which combines the Maximum a Posterior (MAP) estimation with Minimum Mean-Square Error (MMSE), Maximum Likelihood (ML), and Minimum Interval Length (MIL) estimations. In the M4 estimation, our method maximizes the posterior over the state obtained by the MMSE estimation. The method also minimizes interval of the posterior by reducing the gap between the lower and upper bounds of the posterior. The gap is reduced when the likelihood is maximized by the ML estimation and the interval length of the state is minimized by the MIL estimation. The experimental results demonstrate that M4 estimation can be easily integrated into conventional tracking methods and can greatly enhance their tracking accuracy. In several challenging datasets, our method outperforms state-of-the-art tracking methods.

@InProceedings{Kwon_2014_CVPR,author = {Kwon, Junseok and Mu Lee, Kyoung},title = {Interval Tracker: Tracking by Interval Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Visual distracters are detrimental and generally very difficult to handle in target tracking, because they generate false positive candidates for target matching. The resilience of region-based matching to the distracters depends not only on the matching metric, but also on the characteristics of the target region to be matched. The two tasks, i.e., learning the best metric and selecting the distracter-resilient target regions, actually correspond to the attribute selection and spatial selection processes in the human visual perception. This paper presents an initial attempt to unify the modeling of these two tasks for an effective solution, based on the introduction of a new quantity called Soft Visual Margin. As a function of both matching metric and spatial location, it measures the discrimination between the target and its spatial distracters, and characterizes the reliability of matching. Different from other formulations of margin, this new quantity is analytical and is insensitive to noisy data. This paper presents a novel method to jointly determine the best spatial location and the optimal metric. Based on that, a solid distracter-resilient region tracker is designed, and its effectiveness is validated and demonstrated through extensive experiments.

@InProceedings{Jiang_2014_CVPR,author = {Jiang, Nan and Wu, Ying},title = {Unifying Spatial and Attribute Selection for Distracter-Resilient Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jiejie Zhu, Omar Javed, Jingen Liu, Qian Yu, Hui Cheng, Harpreet Sawhney

Abstract: Detecting pedestrians at a distance from large-format wide-area imagery is a challenging problem because of low ground sampling distance (GSD) and low frame rate of the imagery. In such a scenario, the approaches based on appearance cues alone mostly fail because pedestrians are only a few pixels in size. Frame-differencing and optical flow based approaches also give poor detection results due to noise, camera jitter and parallax in aerial videos. To overcome these challenges, we propose a novel approach to extract Multi-scale Intrinsic Motion Structure features from pedestrian's motion patterns for pedestrian detection. The MIMS feature encodes the intrinsic motion properties of an object, which are location, velocity and trajectory-shape invariant. The extracted MIMS representation is robust to noisy flow estimates. In this paper, we give a comparative evaluation of the proposed method and demonstrate that MIMS outperforms the state of the art approaches in identifying pedestrians from low resolution airborne videos.

@InProceedings{Zhu_2014_CVPR,author = {Zhu, Jiejie and Javed, Omar and Liu, Jingen and Yu, Qian and Cheng, Hui and Sawhney, Harpreet},title = {Pedestrian Detection in Low-resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Xinchu Shi, Haibin Ling, Weiming Hu, Chunfeng Yuan, Junliang Xing

Abstract: Interactions between moving targets often provide discriminative clues for multiple target tracking (MTT), though many existing approaches ignore such interactions due to difficulty in effectively handling them. In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization. To solve the resulting global non-convex maximization, we propose an effective and efficient power iteration framework. This solution enjoys two advantages for MTT: First, it allows us to combine the global energy accumulated from individual trajectories and the between-trajectory interaction energy into a united optimization, which can be solved by the proposed power iteration algorithm. Second, the framework is flexible to accommodate various types of pairwise context models and we in fact studied two different context models in this paper. For evaluation, we apply the proposed methods to four public datasets involving different challenging scenarios such as dense aerial borne traffic tracking, dense point set tracking, and semi-crowded pedestrian tracking. In all the experiments, our approaches demonstrate very promising results in comparison with state-of-the-art trackers.

@InProceedings{Shi_2014_CVPR,author = {Shi, Xinchu and Ling, Haibin and Hu, Weiming and Yuan, Chunfeng and Xing, Junliang},title = {Multi-target Tracking with Motion Context in Tensor Power Iteration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Michael Hornacek, Andrew Fitzgibbon, Carsten Rother

Abstract: We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.

@InProceedings{Hornacek_2014_CVPR,author = {Hornacek, Michael and Fitzgibbon, Andrew and Rother, Carsten},title = {SphereFlow: 6 DoF Scene Flow from RGB-D Pairs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We present a fast optical flow algorithm that can handle large displacement motions. Our algorithm is inspired by recent successes of local methods in visual correspondence searching as well as approximate nearest neighbor field algorithms. The main novelty is a fast randomized edge-preserving approximate nearest neighbor field algorithm which propagates self-similarity patterns in addition to offsets. Experimental results on public optical flow benchmarks show that our method is significantly faster than state-of-the-art methods without compromising on quality, especially when scenes contain large motions.

@InProceedings{Bao_2014_CVPR,author = {Bao, Linchao and Yang, Qingxiong and Jin, Hailin},title = {Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Laura Leal-Taixe, Michele Fenzi, Alina Kuznetsova, Bodo Rosenhahn, Silvio Savarese

Abstract: We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals.  At the core of our model lies a learned dictionary of interaction feature strings which capture relationships between the motions of targets. These feature strings, created from low-level image features, lead to a much richer representation of the physical interactions between targets compared to hand-specified social force models that previous works have introduced for tracking. One disadvantage of using social forces is that all pedestrians must be detected in order for the forces to be applied, while our method is able to encode the effect of undetected targets, making the tracker more robust to partial occlusions. The interaction feature strings are used in a Random Forest framework to track targets according to the features surrounding them. Results on six publicly available sequences show that our method outperforms state-of-the-art approaches in multiple people tracking.

@InProceedings{Leal-Taixe_2014_CVPR,author = {Leal-Taixe, Laura and Fenzi, Michele and Kuznetsova, Alina and Rosenhahn, Bodo and Savarese, Silvio},title = {Learning an Image-based Motion Context for Multiple People Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Xiao Liu, Mingli Song, Dacheng Tao, Xingchen Zhou, Chun Chen, Jiajun Bu

Abstract: The desirability of being able to search for specific persons in surveillance videos captured by different cameras has increasingly motivated interest in the problem of person re-identification, which is a critical yet under-addressed challenge in multi-camera tracking systems. The main difficulty of person re-identification arises from the variations in human appearances from different camera views. In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images. The labeled training images carry the relationship between features from different cameras, and the abundant unlabeled training images are introduced to exploit the geometry of the marginal distribution for obtaining robust sparse representation. In the testing phase, the feature of each target image from the probe camera is first encoded by the sparse representation and then recovered in the feature space spanned by the images from the gallery camera. The features of the same person from different cameras are similar following the above transformation. Experimental results on publicly available datasets demonstrate the superiority of our method.

@InProceedings{Liu_2014_CVPR,author = {Liu, Xiao and Song, Mingli and Tao, Dacheng and Zhou, Xingchen and Chen, Chun and Bu, Jiajun},title = {Semi-Supervised Coupled Dictionary Learning for Person Re-identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Chen Kong, Dahua Lin, Mohit Bansal, Raquel Urtasun, Sanja Fidler

Abstract: In this paper we exploit natural sentential descriptions of RGB-D scenes in order to improve 3D semantic parsing. Importantly, in doing so,  we reason about  which particular object each noun/pronoun is referring to in the image.  This allows us to utilize visual information in order  to disambiguate the so-called coreference resolution problem that arises in text.  Towards this goal, we propose a structure prediction model that exploits potentials computed from text and RGB-D imagery to reason about the class of the 3D objects, the  scene type, as well as  to align the nouns/pronouns with the referred visual objects. We demonstrate the effectiveness of our approach on the challenging NYU-RGBD v2 dataset, which we enrich with natural lingual descriptions. We show that our approach significantly improves 3D detection and scene classification accuracy, and is able to reliably estimate the text-to-image alignment. Furthermore, by using textual and visual information, we are also able to successfully deal with coreference in text, improving upon the state-of-the-art  Stanford coreference system.

@InProceedings{Kong_2014_CVPR,author = {Kong, Chen and Lin, Dahua and Bansal, Mohit and Urtasun, Raquel and Fidler, Sanja},title = {What are You Talking About? Text-to-Image Coreference},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Predicting Failures of Vision Systems

Peng Zhang, Jiuling Wang, Ali Farhadi, Martial Hebert, Devi Parikh

Abstract: Computer vision systems today fail frequently. They also fail abruptly without warning or explanation. Alleviating the former has been the primary focus of the community. In this work, we hope to draw the community's attention to the latter, which is arguably equally problematic for real applications. We promote two metrics to evaluate failure prediction. We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems – semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction – on individual input images. We also explore attribute prediction, where classifiers are typically meant to generalize to new unseen categories. We show that ALERT can be useful in predicting failures of this transfer. Finally, we leverage ALERT to improve the performance of a downstream application of attribute prediction: zero-shot learning. We show that ALERT can outperform several strong baselines for zero-shot learning on four datasets.

@InProceedings{Zhang_2014_CVPR,author = {Zhang, Peng and Wang, Jiuling and Farhadi, Ali and Hebert, Martial and Parikh, Devi},title = {Predicting Failures of Vision Systems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yoshitaka Ushiku, Masatoshi Hidaka, Tatsuya Harada

Abstract: In this paper, we would like to evaluate online learning algorithms for large-scale visual recognition using state-of-the-art features which are preselected and held fixed. Today, combinations of high-dimensional features and linear classifiers are widely used for large-scale visual recognition. Numerous so-called mid-level features have been developed and mutually compared on an experimental basis. Although various learning methods for linear classification have also been proposed in the machine learning and natural language processing literature, they have rarely been evaluated for visual recognition.  Therefore, we give guidelines via investigations of state-of-the-art online learning methods of linear classifiers. Many methods have been evaluated using toy data and natural language processing problems such as document classification. Consequently, we gave those methods a unified interpretation from the viewpoint of visual recognition. Results of controlled comparisons indicate three guidelines that might change the pipeline for visual recognition.

@InProceedings{Ushiku_2014_CVPR,author = {Ushiku, Yoshitaka and Hidaka, Masatoshi and Harada, Tatsuya},title = {Three Guidelines of Online Learning for Large-Scale Visual Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Angela Eigenstetter, Masato Takami, Bjorn Ommer

Abstract: A main theme in object detection are currently discriminative part-based models. The powerful model that combines all parts is then typically only feasible for few constituents,  which are in turn iteratively trained to make them as strong as possible. We follow the opposite strategy by randomly sampling a large number of instance specific part classifiers.  Due to their number, we cannot directly train a powerful classifier to combine all parts. Therefore, we randomly group them into fewer, overlapping compositions that are trained using  a maximum-margin approach. In contrast to the common rationale of compositional approaches, we do not aim for semantically meaningful ensembles. Rather we seek randomized compositions  that are discriminative and generalize over all instances of a category. Our approach not only localizes objects in cluttered scenes, but also explains them by parsing with compositions  and their constituent parts.  We conducted experiments on PASCAL VOC07, on the VOC10 evaluation server, and on the MITIndoor scene dataset. To the best of our knowledge, our randomized max-margin compositions (RM2C)  are the currently best performing single class object detector using only HOG features. Moreover, the individual contributions of compositions  and their parts are evaluated in separate experiments that demonstrate their potential.

@InProceedings{Eigenstetter_2014_CVPR,author = {Eigenstetter, Angela and Takami, Masato and Ommer, Bjorn},title = {Randomized Max-Margin Compositions for Visual Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Large-Scale Visual Font Recognition

Guang Chen, Jianchao Yang, Hailin Jin, Jonathan Brandt, Eli Shechtman, Aseem Agarwala, Tony X. Han

Abstract: This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content. Although visual font recognition has many practical applications, it has largely been neglected by the vision community. To address the VFR problem, we construct a large-scale dataset containing 2,420 font classes, which easily exceeds the scale of most image categorization datasets in computer vision. As font recognition is inherently dynamic and open-ended, i.e., new classes and data for existing categories are constantly added to the database over time, we propose a scalable solution based on the nearest class mean classifier (NCM). The core algorithm is built on local feature embedding, local feature metric learning and max-margin template selection, which is naturally amenable to NCM and thus to such open-ended classification problems. The new algorithm can generalize to new classes and new data at little added cost. Extensive experiments demonstrate that our approach is very effective on our synthetic test images, and achieves promising results on real world test images.

@InProceedings{Chen_2014_CVPR,author = {Chen, Guang and Yang, Jianchao and Jin, Hailin and Brandt, Jonathan and Shechtman, Eli and Agarwala, Aseem and Han, Tony X.},title = {Large-Scale Visual Font Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Georgia Gkioxari, Bharath Hariharan, Ross Girshick, Jitendra Malik

Abstract: A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.

@InProceedings{Gkioxari_2014_CVPR,author = {Gkioxari, Georgia and Hariharan, Bharath and Girshick, Ross and Malik, Jitendra},title = {Using k-Poselets for Detecting People and Localizing Their Keypoints},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Describing Textures in the Wild

Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi

Abstract: Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent  texture descriptors, transferring between datasets and tasks; in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD  and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.

@InProceedings{Cimpoi_2014_CVPR,author = {Cimpoi, Mircea and Maji, Subhransu and Kokkinos, Iasonas and Mohamed, Sammy and Vedaldi, Andrea},title = {Describing Textures in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Ramachandruni N. Sandeep, Yashaswi Verma, C. V. Jawahar

Abstract: The notion of relative attributes as introduced by Parikh and Grauman (ICCV, 2011) provides an appealing way of comparing two images based on their visual properties (or attributes) such as "smiling" for  face images, "naturalness" for outdoor images, etc. For learning such attributes, a Ranking SVM based formulation was proposed that uses globally represented pairs of annotated images. In this paper, we extend this idea towards learning relative attributes using local parts that are shared across categories. First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive "significance-coefficient" that represents its discriminative ability with respect to a particular attribute. For each attribute, the  significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method, the new method is shown to achieve significant improvement in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search.

@InProceedings{Sandeep_2014_CVPR,author = {Sandeep, Ramachandruni N. and Verma, Yashaswi and Jawahar, C. V.},title = {Relative Parts: Distinctive Parts for Learning Relative Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Andrea Vedaldi, Siddharth Mahendran, Stavros Tsogkas, Subhransu Maji, Ross Girshick, Juho Kannala, Esa Rahtu, Iasonas Kokkinos, Matthew B. Blaschko, David Weiss, Ben Taskar, Karen Simonyan, Naomi Saphra, Sammy Mohamed

Abstract: We study the problem of understanding objects in detail, intended as recognizing a wide array of fine-grained object attributes. To this end, we introduce a dataset of 7,413 airplanes annotated in detail with parts and their attributes, leveraging images donated by airplane spotters and crowdsourcing both the design and collection of the detailed annotations. We provide a number of insights that should help researchers interested in designing fine-grained datasets for other basic level categories. We show that the collected data can be used to study the relation between part detection and attribute prediction by diagnosing the performance of classifiers that pool information from different parts of an object. We note that the prediction of certain attributes can benefit substantially from accurate part detection. We also show that, differently from previous results in object detection, employing a large number of part templates can improve detection accuracy at the expenses of detection speed. We finally propose a coarse-to-fine  approach to speed up  detection through a hierarchical cascade algorithm.

@InProceedings{Vedaldi_2014_CVPR,author = {Vedaldi, Andrea and Mahendran, Siddharth and Tsogkas, Stavros and Maji, Subhransu and Girshick, Ross and Kannala, Juho and Rahtu, Esa and Kokkinos, Iasonas and Blaschko, Matthew B. and Weiss, David and Taskar, Ben and Simonyan, Karen and Saphra, Naomi and Mohamed, Sammy},title = {Understanding Objects in Detail with Fine-Grained Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Gordon Christie, Amar Parkash, Ujwal Krothapalli, Devi Parikh

Abstract: Computer Vision algorithms make mistakes. In human-centric applications, some mistakes are more annoying to users than others. In order to design algorithms that minimize the annoyance to users, we need access to an annoyance or cost matrix that holds the annoyance of each type of mistake. Such matrices are not readily available, especially for a wide gamut of human-centric applications where annoyance is tied closely to human perception. To avoid having to conduct extensive user studies to gather the annoyance matrix for all possible mistakes, we propose predicting the annoyance of previously unseen mistakes by learning from example mistakes and their corresponding annoyance. We promote the use of attribute-based representations to transfer this knowledge of annoyance. Our experimental results with faces and scenes demonstrate that our approach can predict annoyance more accurately than baselines. We show that as a result, our approach makes less annoying mistakes in a real-world image retrieval application.

@InProceedings{Christie_2014_CVPR,author = {Christie, Gordon and Parkash, Amar and Krothapalli, Ujwal and Parikh, Devi},title = {Predicting User Annoyance Using Visual Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Linear Ranking Analysis

Abstract: We extend the classical linear discriminant analysis (LDA) technique to linear ranking analysis (LRA), by considering the ranking order of classes centroids on the projected subspace. Under the constrain on the ranking order of the classes, two criteria are proposed: 1) minimization of the classification error with the assumption that each class is homogenous Guassian distributed; 2) maximization of the sum (average) of the K minimum distances of all neighboring-class (centroid) pairs. Both criteria can be efficiently solved by the convex optimization for one-dimensional subspace. Greedy algorithm is applied to extend the results to the multi-dimensional subspace. Experimental results show that 1) LRA with both criteria achieve state-of-the-art performance on the tasks of ranking learning and zero-shot learning; and 2) the maximum margin criterion provides a discriminative subspace selection method, which can significantly remedy the class separation problem in comparing with several representative extensions of LDA.

@InProceedings{Deng_2014_CVPR,author = {Deng, Weihong and Hu, Jiani and Guo, Jun},title = {Linear Ranking Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Mattis Paulin, Jerome Revaud, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid

Abstract: A simple approach to learning invariances in image clas- sification consists in augmenting the training set with transformed versions of the original images. However, given a large set of possible transformations, selecting a com- pact subset is challenging. Indeed, all transformations are not equally informative and adding uninformative transfor- mations increases training time with no gain in accuracy. We propose a principled algorithm – Image Transformation Pursuit (ITP) – for the automatic selection of a compact set of transformations. ITP works in a greedy fashion, by se- lecting at each iteration the one that yields the highest accuracy gain. ITP also allows to efficiently explore complex transformations, that combine basic transformations. We report results on two public benchmarks: the CUB dataset of bird images and the ImageNet 2010 challenge. Using Fisher Vector representations, we achieve an improvement from 28.2% to 45.2% in top-1 accuracy on CUB, and an im- provement from 70.1% to 74.9% in top-5 accuracy on Im- ageNet. We also show significant improvements for deep convnet features: from 47.3% to 55.4% on CUB and from 77.9% to 81.4% on ImageNet.

@InProceedings{Paulin_2014_CVPR,author = {Paulin, Mattis and Revaud, Jerome and Harchaoui, Zaid and Perronnin, Florent and Schmid, Cordelia},title = {Transformation Pursuit for Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


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

Abstract: In recent years, large image data sets such as "ImageNet", "TinyImages" or ever-growing social networks like "Flickr" have emerged, posing new challenges to image classification that were not apparent in smaller image sets. In particular, the efficient handling of dynamically growing data sets, where not only the amount of training images, but also the number of classes increases over time, is a relatively unexplored problem. To remedy this, we introduce Nearest Class Mean Forests (NCMF), a variant of Random Forests where the decision nodes are based on nearest class mean (NCM) classification. NCMFs not only outperform conventional random forests, but are also well suited for integrating new classes. To this end, we propose and compare several approaches to incorporate data from new classes, so as to seamlessly extend the previously trained forest instead of re-training them from scratch. In our experiments, we show that NCMFs trained on small data sets with 10 classes can be extended to large data sets with 1000 classes without significant loss of accuracy compared to training from scratch on the full data.

@InProceedings{Ristin_2014_CVPR,author = {Ristin, Marko and Guillaumin, Matthieu and Gall, Juergen and Van Gool, Luc},title = {Incremental Learning of NCM Forests for Large-Scale Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Hakan Bilen, Marco Pedersoli, Vinay P. Namboodiri, Tinne Tuytelaars, Luc Van Gool

Abstract: In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels. Unlike these works, in this paper, we propose to enhance the semantic representation of an image. We aim to learn the most important visual components of an image and how they interact in order to classify the objects correctly. To achieve our objective, we propose a new latent SVM model for category level object classification. Starting from image-level annotations, we jointly learn the object class and its context in terms of spatial location (where) and appearance (what). Furthermore, to regularize the complexity of the model we learn the spatial and  co-occurrence relations between adjacent regions, such that unlikely configurations are penalized. Experimental results demonstrate that the proposed method can consistently enhance results on the challenging Pascal VOC dataset in terms of classification and weakly supervised detection. We also show how semantic representation can be exploited for finding similar content.

@InProceedings{Bilen_2014_CVPR,author = {Bilen, Hakan and Pedersoli, Marco and Namboodiri, Vinay P. and Tuytelaars, Tinne and Van Gool, Luc},title = {Object Classification with Adaptable Regions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak

Abstract: We present the Discriminative Ferns Ensemble (DFE) classifier for efficient visual object recognition. The classifier architecture is designed to optimize both classification speed and accuracy when a large training set is available. Speed is obtained using simple binary features and direct indexing into a set of tables, and accuracy by using a large capacity model and careful discriminative optimization. The proposed framework is applied to the problem of hand pose recognition in depth and infra-red images, using a very large training set. Both the accuracy and the classification time obtained are considerably superior to relevant competing methods, allowing one to reach accuracy targets with run times orders of magnitude faster than the competition. We show empirically that using DFE, we can significantly reduce classification time by increasing training sample size for a fixed target accuracy. Finally a DFE result is shown for the MNIST dataset,  showing the method's merit extends beyond depth images.

@InProceedings{Krupka_2014_CVPR,author = {Krupka, Eyal and Vinnikov, Alon and Klein, Ben and Bar Hillel, Aharon and Freedman, Daniel and Stachniak, Simon},title = {Discriminative Ferns Ensemble for Hand Pose Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Muhammad Zeeshan Zia, Michael Stark, Konrad Schindler

Abstract: Current systems for scene understanding typically represent objects as 2D or 3D bounding boxes. While these representations have proven robust in a variety of applications, they provide only coarse approximations to the true 2D and 3D extent of objects. As a result, object-object interactions, such as occlusions or ground-plane contact, can be represented only superficially. In this paper, we approach the problem of scene understanding from the perspective of 3D shape modeling, and design a 3D scene representation that reasons jointly about the 3D shape of multiple objects. This representation allows to express 3D geometry and occlusion on the fine detail level of individual vertices of 3D wireframe models, and makes it possible to treat dependencies between objects, such as occlusion reasoning, in a deterministic way. In our experiments, we demonstrate the benefit of jointly estimating the 3D shape of multiple objects in a scene over working with coarse boxes, on the recently proposed KITTI dataset of realistic street scenes.

@InProceedings{Zia_2014_CVPR,author = {Zeeshan Zia, Muhammad and Stark, Michael and Schindler, Konrad},title = {Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, Bernt Schiele

Abstract: Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Human Pose" that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities. The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each image we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we perform a detailed analysis of leading human pose estimation approaches and gaining insights for the success and failures of these methods.

@InProceedings{Andriluka_2014_CVPR,author = {Andriluka, Mykhaylo and Pishchulin, Leonid and Gehler, Peter and Schiele, Bernt},title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Marco Pedersoli, Tinne Tuytelaars, Luc Van Gool

Abstract: Face detection and facial points localization are interconnected tasks. Recently it has been shown that solving these two tasks jointly with a mixture of trees of parts (MTP) leads to state-of-the-art results. However, MTP, as most other methods for facial point localization proposed so far, requires a complete annotation of the training data at facial point level. This is used to predefine the structure of the trees and to place the parts correctly. In this work we extend the mixtures from trees to more general loopy graphs. In this way we can learn in a weakly supervised manner (using only the face location and orientation) a powerful deformable detector that implicitly aligns its parts to the detected face in the image. By attaching some reference points to the correct parts of our detector we can then localize the facial points. In terms of detection our method clearly outperforms the state-of-the-art, even if competing with methods that use facial point annotations during training. Additionally, without any facial point annotation at the level of individual training images, our method can localize facial points with an accuracy similar to fully supervised approaches.

@InProceedings{Pedersoli_2014_CVPR,author = {Pedersoli, Marco and Tuytelaars, Tinne and Van Gool, Luc},title = {Using a Deformation Field Model for Localizing Faces and Facial Points under Weak Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Active Annotation Translation

Steve Branson, Kristjan Eldjarn Hjorleifsson, Pietro Perona

Abstract: We introduce a general framework for quickly annotating an image dataset when previous annotations exist. The new annotations (e.g. part locations) may be quite different from the old annotations (e.g. segmentations).  Human annotators may be thought of as helping translate the old annotations into the new ones.  As annotators label images, our algorithm incrementally learns a translator from source to target labels as well as a computer-vision-based structured predictor. These two components are combined to form an improved prediction system which accelerates the annotators' work through a smart GUI.   We show how the method can be applied to translate between a wide variety of annotation types, including bounding boxes, segmentations, 2D and 3D part-based systems, and class and attribute labels. The proposed system will be a useful tool toward exploring new types of representations beyond simple bounding boxes, object segmentations, and class labels, and toward finding new ways to exploit existing large datasets with traditional types of annotations like SUN, Image Net, and Pascal VOC.  Experiments on the CUB-200-2011 and H3D datasets demonstrate 1) our method accelerates collection of part annotations by a factor of 3-20 compared to manual labeling, 2) our system can be used effectively in a scheme where definitions of part, attribute, or action vocabularies are evolved interactively without relabeling the entire dataset, and 3) toward collecting pose annotations, segmentations are more useful than bounding boxes, and part-level annotations are more effective than segmentations.

@InProceedings{Branson_2014_CVPR,author = {Branson, Steve and Eldjarn Hjorleifsson, Kristjan and Perona, Pietro},title = {Active Annotation Translation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Looking Beyond the Visible Scene

Aditya Khosla, Byoungkwon An An, Joseph J. Lim, Antonio Torralba

Abstract: A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald’s or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.

@InProceedings{Khosla_2014_CVPR,author = {Khosla, Aditya and An An, Byoungkwon and Lim, Joseph J. and Torralba, Antonio},title = {Looking Beyond the Visible Scene},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Two-Class Weather Classification

Cewu Lu, Di Lin, Jiaya Jia, Chi-Keung Tang

Abstract: Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy. Never adequately addressed, this twoclass classification problem is by no means trivial given the great variety of outdoor images. Our weather feature combines special cues after properly encoding them into feature vectors. They then work collaboratively in synergy under a unified optimization framework that is aware of the presence (or absence) of a given weather cue during learning and classification. Extensive experiments and comparisons are performed to verify our method. We build a new weather image dataset consisting of 10K sunny and cloudy images, which is available online together with the executable.

@InProceedings{Lu_2014_CVPR,author = {Lu, Cewu and Lin, Di and Jia, Jiaya and Tang, Chi-Keung},title = {Two-Class Weather Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. It is often caused by the complexity of latent image structure when convolving part filters with input images. This problem makes mid-level representation, even after pooling, not distinct enough to classify input data correctly to categories. Our solution is to learn important spatial pooling regions along with their appearance. The experiments show that this new framework suppresses false response and produces improved results on several datasets, including MIT-Indoor, 15-Scene, and UIUC 8-Sport. When combined with global image features, our method achieves state-of-the-art performance on these datasets.

@InProceedings{Lin_2014_CVPR,author = {Lin, Di and Lu, Cewu and Liao, Renjie and Jia, Jiaya},title = {Learning Important Spatial Pooling Regions for Scene Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Lingxi Xie, Jingdong Wang, Baining Guo, Bo Zhang, Qi Tian

Abstract: Scene recognition is a basic task towards image understanding. Spatial Pyramid Matching (SPM) has been shown to be an efficient solution for spatial context modeling. In this paper, we introduce an alternative approach, Orientational Pyramid Matching (OPM), for orientational context modeling. Our approach is motivated by the observation that the 3D orientations of objects are a crucial factor to discriminate indoor scenes. The novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid. Experimental results on challenging scene classification tasks show that OPM achieves the performance comparable with SPM and that OPM and SPM make complementary contributions so that their combination gives the state-of-the-art performance.

@InProceedings{Xie_2014_CVPR,author = {Xie, Lingxi and Wang, Jingdong and Guo, Baining and Zhang, Bo and Tian, Qi},title = {Orientational Pyramid Matching for Recognizing Indoor Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where subjective inconsistent rankings from multiple rankers are associated with each instance. The key idea is to learn a latent preference distribution for each instance. The proposed method mainly includes two steps. The first step is to generate a common preference distribution that is most compatible to all the personal rankings. The second step is to learn a mapping from the instances to the preference distributions. The proposed preference distribution learning (PDL) method is applied to the problem of multilabel ranking for natural scene images. Experimental results show that PDL can effectively incorporate the information given by the inconsistent rankers, and perform remarkably better than the compared state-of-the-art multilabel ranking algorithms.

@InProceedings{Geng_2014_CVPR,author = {Geng, Xin and Luo, Longrun},title = {Multilabel Ranking with Inconsistent Rankers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Joseph Tighe, Marc Niethammer, Svetlana Lazebnik

Abstract: This work proposes a method to interpret a scene by assigning a semantic label at every pixel  and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a set of candidate object masks for a given test image,  we select a subset of objects that explain the image well and have valid overlap relationships and occlusion ordering.  This is done by minimizing an integer quadratic program either using a greedy method or a standard solver. Then we alternate between using the object predictions to refine the pixel labels and vice versa. The proposed system obtains promising results on two challenging subsets of the LabelMe and SUN datasets,  the largest of which contains 45,676 images and 232 classes.

@InProceedings{Tighe_2014_CVPR,author = {Tighe, Joseph and Niethammer, Marc and Lazebnik, Svetlana},title = {Scene Parsing with Object Instances and Occlusion Ordering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yan Deng, Anand Rangarajan, Stephan Eisenschenk, Baba C. Vemuri

Abstract: In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrodinger distance transform (SDT) representation. This is achieved by solving a static Schrodinger equation instead of the corresponding static Hamilton-Jacobi equation in this setting. The SDT representation is an analytic expression and following the theoretical physics literature, can be normalized to have unit L_2 norm---making it a square-root density, which is identified with a point on a unit Hilbert sphere, whose intrinsic geometry is fully known. The Fisher-Rao metric, a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere. In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm.  We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques.  Finally, to evaluate the performance of our algorithm---dubbed SDTM---we present several synthetic and real data examples along with extensive comparisons to state-of-the-art techniques.  The experiments show that our algorithm outperforms state-of the-art point set registration algorithms on many quantitative metrics.

@InProceedings{Deng_2014_CVPR,author = {Deng, Yan and Rangarajan, Anand and Eisenschenk, Stephan and Vemuri, Baba C.},title = {A Riemannian Framework for Matching Point Clouds Represented by the Schrodinger Distance Transform},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Mathieu Aubry, Daniel Maturana, Alexei A. Efros, Bryan C. Russell, Josef Sivic

Abstract: This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online. Using the "chair" class as a running example, we propose an exemplar-based 3D category representation, which can explicitly model chairs of different styles as well as the large variation in viewpoint. We develop an approach to establish part-based correspondences between 3D CAD models and real photographs. This is achieved by (i) representing each 3D model using a set of view-dependent mid-level visual elements learned from synthesized views in a discriminative fashion, (ii) carefully calibrating the individual element detectors on a common dataset of negative images, and (iii) matching visual elements to the test image allowing for small mutual deformations but preserving the viewpoint and style constraints. We demonstrate the ability of our system to align 3D models with 2D objects in the challenging PASCAL VOC images, which depict a wide variety of chairs in complex scenes.

@InProceedings{Aubry_2014_CVPR,author = {Aubry, Mathieu and Maturana, Daniel and Efros, Alexei A. and Russell, Bryan C. and Sivic, Josef},title = {Seeing 3D Chairs: Exemplar Part-based 2D-3D Alignment using a Large Dataset of CAD Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Julian Straub, Guy Rosman, Oren Freifeld, John J. Leonard, John W. Fisher III

Abstract: Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system. Known as the Manhattan-World model, this assumption is widely used in computer vision and robotics. The complexity of many real-world scenes, however, necessitates a more flexible model. We propose a novel probabilistic model that describes the world as a mixture of Manhattan frames: each frame defines a different orthogonal coordinate system. This results in a more expressive model that still exploits the orthogonality constraints. We propose an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that utilizes the geometry of the unit sphere. We demonstrate the versatility of our Mixture-of-Manhattan-Frames model by describing complex scenes using depth images of indoor scenes as well as aerial-LiDAR measurements of an urban center. Additionally, we show that the model lends itself to focal-length calibration of depth cameras and to plane segmentation.

@InProceedings{Straub_2014_CVPR,author = {Straub, Julian and Rosman, Guy and Freifeld, Oren and Leonard, John J. and Fisher, III, John W.},title = {A Mixture of Manhattan Frames: Beyond the Manhattan World},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We propose a novel regularity-driven framework for facade detection from aerial images of urban scenes. Gini-index is used in our work to form an edge-based regularity metric relating regularity and distribution sparsity. Facade regions are chosen so that these local regularities are maximized. We apply a greedy adaptive region expansion procedure for facade region detection and growing, followed by integer quadratic programming for removing overlapping facades to optimize facade coverage. Our algorithm can handle images that have wide viewing angles and contain more than 200 facades per image. The experimental results on images from three different cities (NYC, Rome, San-Francisco) demonstrate superior performance on facade detection in both accuracy and speed over state of the art methods. We also show an application of our facade detection for effective cross-view facade matching.

@InProceedings{Liu_2014_CVPR,author = {Liu, Jingchen and Liu, Yanxi},title = {Local Regularity-driven City-scale Facade Detection from Aerial Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Danhang Tang, Hyung Jin Chang, Alykhan Tejani, Tae-Kyun Kim

Abstract: In this paper we present the Latent Regression Forest (LRF), a novel framework for real-time, 3D hand pose estimation from a single depth image. In contrast to prior forest-based methods, which take dense pixels as input, classify them independently and then estimate joint positions afterwards; our method can be considered as a structured coarse-to-fine search, starting from the centre of mass of a point cloud until locating all the skeletal joints. The searching process is guided by a learnt Latent Tree Model which reflects the hierarchical topology of the hand. Our main contributions can be summarised as follows: (i) Learning the topology of the hand in an unsupervised, data-driven manner. (ii) A new forest-based, discriminative framework for structured search in images, as well as an error regression step to avoid error accumulation. (iii) A new multi-view hand pose dataset containing 180K annotated images from 10 different subjects. Our experiments show that the LRF out-performs state-of-the-art methods in both accuracy and efficiency.

@InProceedings{Tang_2014_CVPR,author = {Tang, Danhang and Jin Chang, Hyung and Tejani, Alykhan and Kim, Tae-Kyun},title = {Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Federica Bogo, Javier Romero, Matthew Loper, Michael J. Black

Abstract: New scanning technologies are increasing the importance of 3D mesh data and the need for algorithms that can reliably align it. Surface registration is important for building full 3D models from partial scans, creating statistical shape models, shape retrieval, and tracking. The problem is particularly challenging for non-rigid and articulated objects like human bodies. While the challenges of real-world data registration are not present in existing synthetic datasets, establishing ground-truth correspondences for real 3D scans is difficult. We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments. We define a new dataset called FAUST that contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. To achieve accurate registration, we paint the subjects with high-frequency textures and use an extensive validation process to ensure accurate ground truth. We find that current shape registration methods have trouble with this real-world data. The dataset and evaluation website are available for research purposes at http://faust.is.tue.mpg.de.

@InProceedings{Bogo_2014_CVPR,author = {Bogo, Federica and Romero, Javier and Loper, Matthew and Black, Michael J.},title = {FAUST: Dataset and Evaluation for 3D Mesh Registration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi

Abstract: We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recently become increasingly popular in computer vision. However, the number of known positive definite kernels on manifolds remain very limited. Furthermore, most kernels typically depend on at least one parameter that needs to be tuned for the problem at hand. A poor choice of kernel, or of parameter value, may yield significant performance drop-off. Here, we show that positive definite radial kernels on the unit $n$-sphere, the Grassmann manifold and Kendall's shape manifold can be expressed in a simple form whose parameters can be automatically optimized within a support vector machine framework. We demonstrate the benefits of our kernel learning algorithm on object, face, action and shape recognition.

@InProceedings{Jayasumana_2014_CVPR,author = {Jayasumana, Sadeep and Hartley, Richard and Salzmann, Mathieu and Li, Hongdong and Harandi, Mehrtash},title = {Optimizing Over Radial Kernels on Compact Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Soren Hauberg, Aasa Feragen, Michael J. Black

Abstract: As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase -- "big data" implies "big outliers''. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA do not scale beyond small-to-medium sized datasets. To address this, we introduce the Grassmann Average (GA), which expresses dimensionality reduction as an average of the subspaces spanned by the data. Because averages can be efficiently computed, we immediately gain scalability. GA is inherently more robust than PCA, but we show that they coincide for Gaussian data. We exploit that averages can be made robust to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. Robustness can be with respect to vectors (subspaces) or elements of vectors; we focus on the latter and use a trimmed average. The resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. The algorithm has low computational complexity and minimal memory requirements, making it scalable to "big noisy data." We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie.

@InProceedings{Hauberg_2014_CVPR,author = {Hauberg, Soren and Feragen, Aasa and Black, Michael J.},title = {Grassmann Averages for Scalable Robust PCA},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jiashi Feng, Zhouchen Lin, Huan Xu, Shuicheng Yan

Abstract: The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily desired for accurate sample clustering but is rather difficult to obtain. Most current state-of-the-art subspace segmentation methods (such as SSC and LRR) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix. In this work, we  directly pursue the block-diagonal structure by proposing a graph Laplacian constraint based formulation, and then develop an efficient stochastic subgradient  algorithm for optimization. Moreover, two new subspace segmentation methods, the block-diagonal SSC and LRR, are devised in this work. To the best of our knowledge, this is the first research attempt to explicitly pursue such a block-diagonal structure. Extensive experiments on face clustering, motion segmentation and graph construction for semi-supervised learning clearly demonstrate the superiority of our novelly proposed subspace segmentation methods.

@InProceedings{Feng_2014_CVPR,author = {Feng, Jiashi and Lin, Zhouchen and Xu, Huan and Yan, Shuicheng},title = {Robust Subspace Segmentation with Block-diagonal Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Outliers are pervasive in many computer vision and pattern recognition problems. Automatically eliminating outliers scattering among practical data collections becomes increasingly important, especially for Internet inspired vision applications. In this paper, we propose a novel one-class learning approach which is robust to contamination of input training data and able to discover the outliers that corrupt one class of data source. Our approach works under a fully unsupervised manner, differing from traditional one-class learning supervised by known positive labels. By design, our approach optimizes a kernel-based max-margin objective which jointly learns a large margin one-class classifier and a soft label assignment for inliers and outliers. An alternating optimization algorithm is then designed to iteratively refine the classifier and the labeling, achieving a provably convergent solution in only a few iterations. Extensive experiments conducted on four image datasets in the presence of artificial and real-world outliers demonstrate that the proposed approach is considerably superior to the state-of-the-arts in obliterating outliers from contaminated one class of images, exhibiting strong robustness at a high outlier proportion up to 60%.

@InProceedings{Liu_2014_CVPR,author = {Liu, Wei and Hua, Gang and Smith, John R.},title = {Unsupervised One-Class Learning for Automatic Outlier Removal},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}