@InProceedings{Cho_2016_CVPR,author = {Cho, Hojin and Sung, Myungchul and Jun, Bongjin},title = {Canny Text Detector: Fast and Robust Scene Text Localization Algorithm},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


@InProceedings{Hu_2016_CVPR,author = {Hu, Di and Li, Xuelong and lu, Xiaoqiang},title = {Temporal Multimodal Learning in Audiovisual Speech Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Andreas Doumanoglou, Rigas Kouskouridas, Sotiris Malassiotis, Tae-Kyun Kim

@InProceedings{Doumanoglou_2016_CVPR,author = {Doumanoglou, Andreas and Kouskouridas, Rigas and Malassiotis, Sotiris and Kim, Tae-Kyun},title = {Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Liuhao Ge, Hui Liang, Junsong Yuan, Daniel Thalmann

@InProceedings{Ge_2016_CVPR,author = {Ge, Liuhao and Liang, Hui and Yuan, Junsong and Thalmann, Daniel},title = {Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Gedas Bertasius, Jianbo Shi, Lorenzo Torresani

@InProceedings{Bertasius_2016_CVPR,author = {Bertasius, Gedas and Shi, Jianbo and Torresani, Lorenzo},title = {Semantic Segmentation With Boundary Neural Fields},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Gellert Mattyus, Shenlong Wang, Sanja Fidler, Raquel Urtasun

@InProceedings{Mattyus_2016_CVPR,author = {Mattyus, Gellert and Wang, Shenlong and Fidler, Sanja and Urtasun, Raquel},title = {HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Bing Shuai, Zhen Zuo, Bing Wang, Gang Wang

@InProceedings{Shuai_2016_CVPR,author = {Shuai, Bing and Zuo, Zhen and Wang, Bing and Wang, Gang},title = {DAG-Recurrent Neural Networks For Scene Labeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


@InProceedings{Lai_2016_CVPR,author = {Lai, Baisheng and Gong, Xiaojin},title = {Saliency Guided Dictionary Learning for Weakly-Supervised Image Parsing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, Alan L. Yuille

Abstract: Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state-of-the-art performance on semantic image segmentation. One common way to extract multi-scale features is to feed multiple resized input images to a shared deep network and then merge the resulting features for pixel-wise classification. In this work, we propose an attention mechanism that learns to softly weight the multi-scale features at each pixel location. We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model. The proposed attention model not only outperforms average- and max-pooling, but allows us to diagnostically visualize the importance of features at different positions and scales. Moreover, we show that adding extra supervision to the output at each scale is essential to achieving excellent performance when merging multi-scale features. We demonstrate the effectiveness of our model with extensive experiments on three challenging datasets, including PASCAL-Person-Part, PASCAL VOC 2012 and a subset of MS-COCO 2014.

@InProceedings{Chen_2016_CVPR,author = {Chen, Liang-Chieh and Yang, Yi and Wang, Jiang and Xu, Wei and Yuille, Alan L.},title = {Attention to Scale: Scale-Aware Semantic Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Scene labeling task is to segment the image into meaningful regions and categorize them into classes of objects which comprised the image. Commonly used methods typically find the local features for each segment and label them using classifiers. Afterwards, labeling is smoothed in order to make sure that neighboring regions receive similar labels. However, these methods ignore expressive connections between labels and non-local dependencies among regions. In this paper, we propose to use a sparse estimation of precision matrix (also called concentration matrix), which is the inverse of covariance matrix  of data obtained by graphical lasso to find interaction between labels and regions. To do this, we formulate the problem as an energy minimization over a graph, whose structure is captured by applying sparse constraint on the elements of the precision matrix. This graph encodes (or represents) only significant interactions and avoids a fully connected graph, which is typically used to reflect the long distance associations. We use local and global information to achieve better labeling. We assess our approach on three datasets and obtained promising results.

@InProceedings{Souly_2016_CVPR,author = {Souly, Nasim and Shah, Mubarak},title = {Scene Labeling Using Sparse Precision Matrix},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Iterative Instance Segmentation

Ke Li, Bharath Hariharan, Jitendra Malik

Abstract: Existing methods for pixel-wise labelling tasks generally disregard the underlying structure of labellings, often leading to predictions that are visually implausible. While incorporating structure into the model should improve prediction quality, doing so is challenging - manually specifying the form of structural constraints may be impractical and inference often becomes intractable even if structural constraints are given. We sidestep this problem by reducing structured prediction to a sequence of unconstrained prediction problems and demonstrate that this approach is capable of automatically discovering priors on shape, contiguity of region predictions and smoothness of region contours from data without any a priori specification. On the instance segmentation task, this method outperforms the state-of-the-art, achieving a mean AP^r of 63.6% at 50% overlap and 43.3% at 70% overlap.

@InProceedings{Li_2016_CVPR,author = {Li, Ke and Hariharan, Bharath and Malik, Jitendra},title = {Iterative Instance Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


@InProceedings{Kuen_2016_CVPR,author = {Kuen, Jason and Wang, Zhenhua and Wang, Gang},title = {Recurrent Attentional Networks for Saliency Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Guillaume Seguin, Piotr Bojanowski, Remi Lajugie, Ivan Laptev

@InProceedings{Seguin_2016_CVPR,author = {Seguin, Guillaume and Bojanowski, Piotr and Lajugie, Remi and Laptev, Ivan},title = {Instance-Level Video Segmentation From Object Tracks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Jun Xie, Martin Kiefel, Ming-Ting Sun, Andreas Geiger

@InProceedings{Xie_2016_CVPR,author = {Xie, Jun and Kiefel, Martin and Sun, Ming-Ting and Geiger, Andreas},title = {Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Amir Kolaman, Maxim Lvov, Rami Hagege, Hugo Guterman

@InProceedings{Kolaman_2016_CVPR,author = {Kolaman, Amir and Lvov, Maxim and Hagege, Rami and Guterman, Hugo},title = {Amplitude Modulated Video Camera - Light Separation in Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Boxin Shi, Zhe Wu, Zhipeng Mo, Dinglong Duan, Sai-Kit Yeung, Ping Tan

@InProceedings{Shi_2016_CVPR,author = {Shi, Boxin and Wu, Zhe and Mo, Zhipeng and Duan, Dinglong and Yeung, Sai-Kit and Tan, Ping},title = {A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Ting-Chun Wang, Manohar Srikanth, Ravi Ramamoorthi

@InProceedings{Wang_2016_CVPR,author = {Wang, Ting-Chun and Srikanth, Manohar and Ramamoorthi, Ravi},title = {Depth From Semi-Calibrated Stereo and Defocus},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Ying Fu, Yinqiang Zheng, Imari Sato, Yoichi Sato

Abstract: Conventional scanning and multiplexing techniques for hyperspectral imaging suffer from limited temporal and/or spatial resolution. To resolve this issue, coding techniques are becoming increasingly popular in developing   snapshot systems for high-resolution hyperspectral imaging. For such systems, it is    a critical task to accurately restore the 3D hyperspectral image    from its corresponding coded 2D image. In this paper, we propose an    effective method for coded hyperspectral image restoration, which exploits extensive structure sparsity in the hyperspectral image. Specifically, we simultaneously explore spectral and spatial correlation via low-rank regularizations, and formulate the restoration problem into a variational optimization model, which can be solved via an iterative numerical algorithm. Experimental   results using both synthetic data and real images show that the proposed method can significantly outperform the    state-of-the-art methods on several popular coding-based hyperspectral imaging systems.

@InProceedings{Fu_2016_CVPR,author = {Fu, Ying and Zheng, Yinqiang and Sato, Imari and Sato, Yoichi},title = {Exploiting Spectral-Spatial Correlation for Coded Hyperspectral Image Restoration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Julie Chang, Isaac Kauvar, Xuemei Hu, Gordon Wetzstein

Abstract: Light fields have many applications in machine vision, consumer photography, robotics, and microscopy. However, the prevalent resolution limits of existing light field imaging systems hinder widespread adoption. In this paper, we analyze fundamental resolution limits of light field cameras in the diffraction limit. We propose a sequential, coded-aperture-style acquisition scheme that optimizes the resolution of a light field reconstructed from multiple photographs captured from different perspectives and f-number settings. We also show that the proposed acquisition scheme facilitates high dynamic range light field imaging and demonstrate a proof-of-concept prototype system. With this work, we hope to advance our understanding of the resolution limits of light field photography and develop practical computational imaging systems to overcome them.

@InProceedings{Chang_2016_CVPR,author = {Chang, Julie and Kauvar, Isaac and Hu, Xuemei and Wetzstein, Gordon},title = {Variable Aperture Light Field Photography: Overcoming the Diffraction-Limited Spatio-Angular Resolution Tradeoff},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Convolutional Neural Networks (CNNs) have recently been successfully applied to various Computer Vision (CV) applications. In this paper we utilize CNNs to predict depth information for given Light Field (LF) data. The proposed method learns an end-to-end mapping between the 4D light field and a representation of the corresponding 4D depth field in terms of 2D hyperplane orientations. The obtained prediction is then further refined in a post processing step by applying a higher-order regularization. Existing LF datasets are not sufficient for the purpose of the training scheme tackled in this paper. This is mainly due to the fact that the ground truth depth of existing datasets is inaccurate and/or the datasets are limited to a small number of LFs. This made it necessary to generate a new synthetic LF dataset, which is based on the raytracing software POV-Ray. This new dataset provides floating point accurate ground truth depth fields, and due to a random scene generator the dataset can be scaled as required.

@InProceedings{Heber_2016_CVPR,author = {Heber, Stefan and Pock, Thomas},title = {Convolutional Networks for Shape From Light Field},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Rajat Aggarwal, Amrisha Vohra, Anoop M. Namboodiri

Abstract: We present a practical solution for generating 360 degree stereo panoramic videos using a single camera. Current approaches either use a moving camera that captures multiple images of a scene, which are then stitched together to form the final panorama, or use multiple cameras that are synchronized. A moving camera limits the solution to static scenes, while multi-camera solutions require dedicated calibrated setups. Our approach improves upon the existing solutions in two significant ways: It solves the problem using a single camera, thus minimizing the calibration problem and providing us the ability to convert any digital camera into a panoramic stereo capture device. It captures all the light rays required for stereo panoramas in a single frame using a compact custom designed mirror, thus making the design practical to manufacture and easier to use. We analyze several properties of the design as well as present panoramic stereo and depth estimation results.

@InProceedings{Aggarwal_2016_CVPR,author = {Aggarwal, Rajat and Vohra, Amrisha and Namboodiri, Anoop M.},title = {Panoramic Stereo Videos With a Single Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


The Next Best Underwater View

Abstract: To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in a participating medium. Moreover, underwater and in other media, artificial lighting is needed. Overall, data quality depends on the observed surface, medium and the time-varying poses of the camera and light source. This work proposes to optimize camera and light poses as they move, so that the surface is scanned efficiently and the descattered recovery has the highest quality. The work generalizes the next best view concept of robot vision to scattering media and cooperative movable lighting. It also extends descattering to platforms that move optimally. The optimization criterion is information gain, taken from information theory. We exploit the existence of a prior rough 3D model, since underwater such a model is routinely obtained using sonar. We demonstrate this principle in a scaled-down setup.

@InProceedings{Sheinin_2016_CVPR,author = {Sheinin, Mark and Schechner, Yoav Y.},title = {The Next Best Underwater View},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Yoshie Kobayashi, Tetsuro Morimoto, Imari Sato, Yasuhiro Mukaigawa, Takao Tomono, Katsushi Ikeuchi

Abstract: Reconstruction of shapes and appearances of thin film objects can be applied to many fields such as industrial inspection, biological analysis, and archeology research. However, it comes with many challenging issues because the appearances of thin film can change dramatically depending on view and light directions. The appearance is deeply dependent on not only the shapes but also the optical parameters of thin film. In this paper, we propose a novel method to estimate shapes and film thickness. First, we narrow down candidates of zenith angle by degree of polarization and determine it by the intensity of thin film which increases monotonically along the zenith angle. Second, we determine azimuth angle from occluding boundaries. Finally, we estimate the film thickness by comparing a look-up table of color along the thickness and zenith angle with captured images. We experimentally evaluated the accuracy of estimated shapes and appearances and found that our proposed method is effective.

@InProceedings{Kobayashi_2016_CVPR,author = {Kobayashi, Yoshie and Morimoto, Tetsuro and Sato, Imari and Mukaigawa, Yasuhiro and Tomono, Takao and Ikeuchi, Katsushi},title = {Reconstructing Shapes and Appearances of Thin Film Objects Using RGB Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Tomas F. Yago Vicente, Minh Hoai, Dimitris Samaras

Abstract: Recent shadow detection algorithms have shown initial success on small datasets of images from specific domains. However, shadow detection on broader image domains is still challenging due to the lack of annotated training data. This is due to the intense manual labor in annotating shadow data. In this paper we propose "lazy annotation", an efficient annotation method where an annotator only needs to mark the important shadow areas and some non-shadow areas. This yields data with noisy labels that are not yet useful for training a shadow detector. We address the problem of label noise by jointly learning a shadow region classifier and recovering the labels in the training set. We consider the training labels as unknowns and formulate the label recovery problem as the minimization of the sum of squared leave-one-out errors of a Least Squares SVM, which can be efficiently optimized. Experimental results show that a classifier trained with recovered labels achieves comparable performance to a classifier trained on the properly annotated data. These results suggest a feasible approach to address the task of detecting shadows in an unfamiliar domain: collecting and lazily annotating some images from the new domain for training. As will be demonstrated, this approach outperforms methods that rely on precisely annotated but less relevant datasets. Initial results suggest more general applicability.

@InProceedings{Vicente_2016_CVPR,author = {Vicente, Tomas F. Yago and Hoai, Minh and Samaras, Dimitris},title = {Noisy Label Recovery for Shadow Detection in Unfamiliar Domains},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Recognizing Car Fluents From Video

Bo Li, Tianfu Wu, Caiming Xiong, Song-Chun Zhu

Abstract: Physical fluents, a term originally used by Newton [40], refers to time-varying object states in dynamic scenes. In this paper, we are interested in inferring the fluents of vehicles from video. For example, a door (hood, trunk) is open or closed through various actions, light is blinking to turn. Recognizing these fluents has broad applications, yet have received scant attention in the computer vision literature. Car fluent recognition entails a unified framework for car detection, car part localization and part status recognition, which is made difficult by large structural and appearance variations, low resolutions and occlusions. This paper learns a spatial-temporal And-Or hierarchical model to represent car fluents. The learning of this model is formulated under the latent structural SVM framework. Since there are no publicly related dataset, we collect and annotate a car fluent dataset consisting of car videos with diverse fluents. In experiments, the proposed method outperforms several highly related baseline methods in terms of car fluent recognition and car part localization.

@InProceedings{Li_2016_CVPR,author = {Li, Bo and Wu, Tianfu and Xiong, Caiming and Zhu, Song-Chun},title = {Recognizing Car Fluents From Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Oscar Koller, Hermann Ney, Richard Bowden

Abstract: This work presents a new approach to learning a frame-based classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.

@InProceedings{Koller_2016_CVPR,author = {Koller, Oscar and Ney, Hermann and Bowden, Richard},title = {Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Edward Johns, Stefan Leutenegger, Andrew J. Davison

Abstract: A multi-view image sequence provides a much richer capacity for object recognition than from a single image. However, most existing solutions to multi-view recognition typically adopt hand-crafted, model-based geometric methods, which do not readily embrace recent trends in deep learning. We propose to bring Convolutional Neural Networks to generic multi-view recognition, by decomposing an image sequence into a set of image pairs, classifying each pair independently, and then learning an object classifier by weighting the contribution of each pair. This allows for recognition over arbitrary camera trajectories, without requiring explicit training over the potentially infinite number of camera paths and lengths. Building these pairwise relationships then naturally extends to the next-best-view problem in an active recognition framework. To achieve this, we train a second Convolutional Neural Network to map directly from an observed image to next viewpoint. Finally, we incorporate this into a trajectory optimisation task, whereby the best recognition confidence is sought for a given trajectory length. We present state-of-the-art results in both guided and unguided multi-view recognition on the ModelNet dataset, and show how our method can be used with depth images, greyscale images, or both.

@InProceedings{Johns_2016_CVPR,author = {Johns, Edward and Leutenegger, Stefan and Davison, Andrew J.},title = {Pairwise Decomposition of Image Sequences for Active Multi-View Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Yixin Zhu, Chenfanfu Jiang, Yibiao Zhao, Demetri Terzopoulos, Song-Chun Zhu

Abstract: We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects. Rather than defining affordance in terms of the geometric compatibility between body poses and 3D objects, we devise algorithms that employ physics-based simulation to infer the relevant forces/pressures acting on body parts. By observing the choices people make in videos (particularly in selecting a chair in which to sit) our system learns the comfort intervals of the forces exerted on body parts (while sitting). We account for people's preferences in terms of human utilities, which transcend comfort intervals to account also for meaningful tasks within scenes and spatiotemporal constraints in motion planning, such as for the purposes of robot task planning.

@InProceedings{Zhu_2016_CVPR,author = {Zhu, Yixin and Jiang, Chenfanfu and Zhao, Yibiao and Terzopoulos, Demetri and Zhu, Song-Chun},title = {Inferring Forces and Learning Human Utilities From Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Hyun Soo Park, jyh-Jing Hwang, Jianbo Shi

Abstract: A first-person video can generate powerful physical sensations of action in an observer. In this paper, we focus on a problem of Force from Motion---decoding the sensation of 1) passive forces such as the gravity, 2) the physical scale of the motion (speed) and space, and 3) active forces exerted by the observer such as pedaling a bike or banking on a ski turn.    The sensation of gravity can be observed in a natural image. We learn this image cue for predicting a gravity direction in a 2D image and integrate the prediction across images to estimate the 3D gravity direction using structure from motion. The sense of physical scale is revealed to us when the body is in a dynamically balanced state. We compute the unknown physical scale of 3D reconstructed camera motion by leveraging the torque equilibrium at a banked turn that relates the centripetal force, gravity, and the body leaning angle. The active force and torque governs 3D egomotion through the physics of rigid body dynamics. Using an inverse dynamics optimization, we directly minimize 2D reprojection error (in video) with respect to 3D world structure, active forces, and additional passive forces such as air drag and friction force. We use structure from motion with the physical scale and gravity direction as an initialization of our bundle adjustment for force estimation. Our method shows quantitatively equivalent reconstruction comparing to IMU measurements in terms of gravity and scale recovery and outperforms method based on 2D optical flow for an active action recognition task. We apply our method to first person videos of mountain biking, urban bike racing, skiing, speedflying with parachute, and wingsuit flying where inertial measurements are not accessible.

@InProceedings{Park_2016_CVPR,author = {Park, Hyun Soo and Hwang, jyh-Jing and Shi, Jianbo},title = {Force From Motion: Decoding Physical Sensation in a First Person Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging temporal coherence, where the image representations of temporally close frames to exhibit only small differences.  While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture *how* the visual content changes.  We propose to generalize slow feature analysis to "steady" feature analysis.  The key idea is to impose a prior that higher order derivatives in the learned feature space must be small.  To this end, we train a convolutional neural network with a regularizer that minimizes a contrastive loss on tuples of sequential frames from unlabeled video.  Focusing on the case of triplets of frames, the proposed method encourages that feature changes over time should be smooth, i.e., similar to the most recent changes.  Using five diverse image and video datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object recognition, scene classification, and action recognition tasks.  We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.

@InProceedings{Jayaraman_2016_CVPR,author = {Jayaraman, Dinesh and Grauman, Kristen},title = {Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Pan Ji, Hongdong Li, Mathieu Salzmann, Yiran Zhong

Abstract: Feature tracking is a fundamental problem in computer vision with applications in various tasks including 3D reconstruction and visual SLAM. While many methods have been devoted to making these tasks robust to noise and outliers, less attention has been attracted to improving the feature tracking itself.  This paper introduces a novel multi-body feature tracker that takes advantage of the multi-body rigidity assumption to improve tracking robustness. A conventional approach to addressing this problem would consist of alternating between solving two subtasks: motion segmentation and feature tracking under rigidity constraints for each segment. This approach, however, requires knowing the number of motions, as well as assigning points to motion groups, which is typically sensitive to the motion estimates. By contrast, here, we introduce a segmentation-free solution to multi-body feature tracking that bypasses the motion assignment step and reduces to solving a series of subproblems with closed-form solutions. Our experiments demonstrate the benefits of our approach in terms of tracking accuracy and robustness to noise.

@InProceedings{Ji_2016_CVPR,author = {Ji, Pan and Li, Hongdong and Salzmann, Mathieu and Zhong, Yiran},title = {Robust Multi-Body Feature Tracker: A Segmentation-Free Approach},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Volumetric 3D Tracking by Detection

Chun-Hao Huang, Benjamin Allain, Jean-Sebastien Franco, Nassir Navab, Slobodan Ilic, Edmond Boyer

Abstract: In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations. On one hand, 3D tracking by detection has shown robust use in the context of interaction (Kinect) and surface tracking. On the other hand, volumetric representations have recently been proven efficient both for building 3D features and for addressing the 3D tracking problem. We leverage these benefits by unifying both families of approaches into a single, fully volumetric tracking-by-detection framework. We use a centroidal Voronoi tessellation (CVT) representation to compactly tessellate shapes with optimal discretization, construct a feature space, and perform the tracking according to the correspondences provided by trained random forests. Our results show improved tracking and training computational efficiency and improved memory performance. This in turn enables the use of larger training databases than state of the art approaches, which we leverage by proposing a cross-tracking subject training scheme to benefit from all subject sequences for all tracking situations, thus yielding better detection and less overfitting.

@InProceedings{Huang_2016_CVPR,author = {Huang, Chun-Hao and Allain, Benjamin and Franco, Jean-Sebastien and Navab, Nassir and Ilic, Slobodan and Boyer, Edmond},title = {Volumetric 3D Tracking by Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Shoou-I Yu, Deyu Meng, Wangmeng Zuo, Alexander Hauptmann

Abstract: We propose an identity-aware multi-object tracker based on the solution path algorithm. Our tracker not only produces identity-coherent trajectories based on cues such as face recognition, but also has the ability to pinpoint potential tracking errors. The tracker is formulated as a quadratic optimization problem with L0 norm constraints, which we propose to solve with the solution path algorithm. The algorithm successively solves the same optimization problem but under different Lp norm constraints, where p gradually decreases from 1 to 0. Inspired by the success of the solution path algorithm in various machine learning tasks, this strategy is expected to converge to a better local minimum than directly minimizing the hardly solvable L0 norm or the roughly approximated L1 norm constraints. Furthermore, the acquired solution path complies with the "decision making process" of the tracker, which provides more insight to locating potential tracking errors. Experiments show that not only is our proposed tracker effective, but also the solution path enables automatic pinpointing of potential tracking failures, which can be readily utilized in an active learning framework to improve identity-aware multi-object tracking.

@InProceedings{Yu_2016_CVPR,author = {Yu, Shoou-I and Meng, Deyu and Zuo, Wangmeng and Hauptmann, Alexander},title = {The Solution Path Algorithm for Identity-Aware Multi-Object Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Tianzhu Zhang, Adel Bibi, Bernard Ghanem

Abstract: Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

@InProceedings{Zhang_2016_CVPR,author = {Zhang, Tianzhu and Bibi, Adel and Ghanem, Bernard},title = {In Defense of Sparse Tracking: Circulant Sparse Tracker},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Laura Sevilla-Lara, Deqing Sun, Varun Jampani, Michael J. Black

Abstract: Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. Here we exploit recent advances in static semantic scene segmentation to segment the image into objects of different types. We define different models of image motion in these regions depending on the type of object. For example, the road motion with homographies, vegetation with spatially smooth flow, and independently moving objects like cars and planes with affine+deviations. We then pose the flow estimation problem using a novel formulation of  localized layers, which addresses limitations of traditional layered models for dealing with complex scene motion. Our  semantic flow method achieves the lowest error of any published method in the KITTI-2015 flow benchmark and produces qualitatively better flow and segmentation than recent top methods on a wide range of natural videos.

@InProceedings{Sevilla-Lara_2016_CVPR,author = {Sevilla-Lara, Laura and Sun, Deqing and Jampani, Varun and Black, Michael J.},title = {Optical Flow With Semantic Segmentation and Localized Layers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Video Segmentation via Object Flow

Yi-Hsuan Tsai, Ming-Hsuan Yang, Michael J. Black

Abstract: Video object segmentation is challenging due to fast moving objects, deforming shapes, and cluttered backgrounds. Optical flow can be used to propagate an object segmentation over time but, unfortunately, flow is often inaccurate, particularly around object boundaries. Such boundaries are precisely where we want our segmentation to be accurate. To obtain accurate segmentation across time, we propose an efficient algorithm that considers video segmentation and optical flow estimation simultaneously. For video segmentation, we formulate a principled, multi-scale, spatio-temporal objective function that uses optical flow to propagate information between frames. For optical flow estimation, particularly at object boundaries, we compute the flow independently in the segmented regions and recompose the results. We call the process object flow and demonstrate the effectiveness of jointly optimizing optical flow and video segmentation using an iterative scheme. Experiments on the SegTrack v2 and Youtube-Objects datasets show that the proposed algorithm performs favorably against the other state-of-the-art methods.

@InProceedings{Tsai_2016_CVPR,author = {Tsai, Yi-Hsuan and Yang, Ming-Hsuan and Black, Michael J.},title = {Video Segmentation via Object Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Marc T. Law, YaoLiang Yu, Matthieu Cord, Eric P. Xing

Abstract: Clustering is the task of grouping a set of objects so that objects in the same cluster are more similar to each other than to those in other clusters. The crucial step in most clustering algorithms is to find an appropriate similarity metric, which is both challenging and problem-dependent. Supervised clustering approaches, which can exploit labeled clustered training data that share a common metric with the test set, have thus been proposed. Unfortunately, current metric learning approaches for supervised clustering do not scale to large or even medium-sized datasets. In this paper, we propose a new structured Mahalanobis Distance Metric Learning method for supervised clustering. We formulate our problem as an instance of large margin structured prediction and prove that it can be solved very efficiently in closed-form. The complexity of our method is (in most cases) linear in the size of the training dataset. We further reveal a striking similarity between our approach and multivariate linear regression. Experiments on both synthetic and real datasets confirm several orders of magnitude speedup while still achieving state-of-the-art performance.

@InProceedings{Law_2016_CVPR,author = {Law, Marc T. and Yu, YaoLiang and Cord, Matthieu and Xing, Eric P.},title = {Closed-Form Training of Mahalanobis Distance for Supervised Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Subspace clustering methods based on ell_1, l_2 or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success. However, the choice of the regularizer can greatly impact both theory and practice. For instance, ell_1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no connections between points from different subspaces) under broad conditions e.g., arbitrary subspaces and corrupted data). However, it requires solving a large scale convex optimization problem. On the other hand, l_2 and nuclear norm regularization provide efficient closed form solutions, but require very strong assumptions to guarantee a subspace-preserving affinity, e.g., independent subspaces and uncorrupted data. In this paper we study a subspace clustering method based on orthogonal matching pursuit. We show that the method is both computationally efficient and guaranteed to give a subspace-preserving affinity under broad conditions. Experiments on synthetic data verify our theoretical analysis, and applications in handwritten digit and face clustering show that our approach achieves the best trade off between accuracy and efficiency. Moreover, our approach is the first one to handle 100,000 data points.

@InProceedings{You_2016_CVPR,author = {You, Chong and Robinson, Daniel and Vidal, Rene},title = {Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Chong You, Chun-Guang Li, Daniel P. Robinson, Rene Vidal

Abstract: State-of-the-art subspace clustering methods are based on expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with l_1, l_2 or nuclear norms. l_1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no connections between points from different subspaces) under broad theoretical conditions, but the clusters may not be connected. l_2 and nuclear norm regularization often improve connectivity, but give a subspace-preserving affinity only for independent subspaces. Mixed l_1, l_2 and nuclear norm regularizations offer a balance between the subspace-preserving and connectedness properties, but this comes at the cost of increased computational complexity. This paper studies the geometry of the elastic net regularizer (a mixture of the l_1 and l_2 norms) and uses it to derive a provably correct and scalable active set method for finding the optimal coefficients. Our geometric analysis also provides a theoretical justification and a geometric interpretation for the balance between the connectedness (due to l_2 regularization) and subspace-preserving (due to l_1 regularization) properties for elastic net subspace clustering. Our experiments show that the proposed active set method not only achieves state-of-the-art clustering performance, but also efficiently handles large-scale datasets.

@InProceedings{You_2016_CVPR,author = {You, Chong and Li, Chun-Guang and Robinson, Daniel P. and Vidal, Rene},title = {Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Wenbing Huang, Fuchun Sun, Lele Cao, Deli Zhao, Huaping Liu, Mehrtash Harandi

Abstract: Linear Dynamical Systems (LDSs) are the fundamental tools for encoding spatio-temporal data in various disciplines. To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs. Rather than approximate the extended observability with a finite-order matrix, we represent the space of LDSs by an infinite Grassmannian consisting of the orthonormalized extended observability subspaces. Via a homeomorphic mapping, such Grassmannian is embedded into the space of symmetric matrices, where a tractable objective function can be derived for sparse coding. Then, we propose an efficient method to learn the system parameters of the dictionary atoms explicitly, by imposing the symmetric constraint to the transition matrices of the data and dictionary systems. Moreover, we combine the state covariance into the algorithm formulation, thus further promoting the performance of the models with symmetric transition matrices. Comparative experimental evaluations reveal the superior performance of proposed methods on various tasks including video classification and tactile recognition.

@InProceedings{Huang_2016_CVPR,author = {Huang, Wenbing and Sun, Fuchun and Cao, Lele and Zhao, Deli and Liu, Huaping and Harandi, Mehrtash},title = {Sparse Coding and Dictionary Learning With Linear Dynamical Systems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Thomas Mollenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers

Abstract: We propose a novel spatially continuous framework for convex relaxations based on functional lifting. Our method can be interpreted as a sublabel-accurate solution to multilabel problems. We show that previously proposed functional lifting methods optimize an energy which is linear between two labels and hence require (often infinitely) many labels for a faithful approximation. In contrast, the proposed formulation is based on a piecewise convex approximation and therefore needs far fewer labels - see Fig. 1. In comparison to recent MRF-based approaches, our method is formulated in a spatially continuous setting and shows less grid bias. Moreover, in a local sense, our formulation is the tightest possible convex relaxation. It is easy to implement and allows an efficient primal-dual optimization on GPUs. We show the effectiveness of our approach on several computer vision problems.

@InProceedings{Mollenhoff_2016_CVPR,author = {Mollenhoff, Thomas and Laude, Emanuel and Moeller, Michael and Lellmann, Jan and Cremers, Daniel},title = {Sublabel-Accurate Relaxation of Nonconvex Energies},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Deep learning techniques are renowned for supporting effective transfer learning. However, as we demonstrate, the transferred representations support only a few modes of separation and much of its dimensionality is unutilized. In this work we suggest to learn, in the source domain, multiple orthogonal classifiers. We prove that this leads to a reduced rank representation, which however supports more  discriminative directions. Interestingly, the softmax probabilities produced by the multiple classifiers are likely to be identical. Extensive experimental results further demonstrate the effectiveness of our method.

@InProceedings{Littwin_2016_CVPR,author = {Littwin, Etai and Wolf, Lior},title = {The Multiverse Loss for Robust Transfer Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Can we learn about object classes in images by looking at a collection of relevant 3D models? Or if we want to learn about human (inter-)actions in images, can we benefit from videos or abstract illustrations that show these actions? A common aspect of these settings is the availability of additional or privileged data that can be exploited at training time and that will not be available and not of interest at test time. We seek to generalize the learning with privileged information (LUPI) framework, which requires additional information to be defined per image, to the setting where additional information is a data collection about the task of interest. Our framework minimises the distribution mismatch between errors made in images and in privileged data. The proposed method is tested on four publicly available datasets: Image+ClipArt, Image+3Dobject, and Image+Video. Experimental results reveal that our new LUPI paradigm naturally addresses the cross-dataset learning.

@InProceedings{Sharmanska_2016_CVPR,author = {Sharmanska, Viktoriia and Quadrianto, Novi},title = {Learning From the Mistakes of Others: Matching Errors in Cross-Dataset Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Rudrasis Chakraborty, Dohyung Seo, Baba C. Vemuri

Abstract: Manifold-valued datasets are widely encountered in many computer vision tasks. A non-linear analog of the PCA algorithm, called the Principal Geodesic Analysis (PGA) algorithm suited for data lying on Riemannian manifolds was reported in literature a decade ago. Since the objective function in the PGA algorithm is highly non-linear and hard to solve efficiently in general, researchers have proposed a linear approximation. Though this linear approximation is easy to compute, it lacks accuracy especially when the data exhibits a large variance. Recently, an alternative called the exact PGA was proposed which tries to solve the optimization without any linearization. For general Riemannian manifolds, though it yields a better accuracy than the original (linearized) PGA, for data that exhibit large variance, the optimization is not computationally efficient. In this paper, we propose an efficient exact PGA algorithm for constant curvature Riemannian manifolds (CCM-EPGA). The CCM-EPGA algorithm differs significantly from existing PGA algorithms in two aspects, (i) the distance between a given manifold-valued data point and the principal submanifold is computed analytically and thus no optimization is required as in the existing methods. (ii) Unlike the existing PGA algorithms, the descent into codimension-1 submanifolds does not require any optimization but is accomplished through the use of the Rimeannian inverse Exponential map and the parallel transport operations. We present theoretical and experimental results for constant curvature Riemannian manifolds depicting favorable performance of the CCM-EPGA algorithm compared to existing PGA algorithms. We also present data reconstruction from the principal components which has not been reported in literature in this setting.

@InProceedings{Chakraborty_2016_CVPR,author = {Chakraborty, Rudrasis and Seo, Dohyung and Vemuri, Baba C.},title = {An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Randomized classification trees are among the most popular machine learning tools and found successful applications in many areas. Although this classifier was originally designed as offline learning algorithm, there has been an increased interest in the last years to provide an online variant. In this paper, we propose an online learning algorithm for classification trees that adheres to Bayesian principles. In contrast to state-of-the-art approaches that produce large forests with complex trees, we aim at constructing small ensembles consisting of shallow trees with high generalization capabilities. Experiments on benchmark machine learning and body part recognition datasets show superior performance over state-of-the-art approaches.

@InProceedings{Bulo_2016_CVPR,author = {Bulo, Samuel Rota and Kontschieder, Peter},title = {Online Learning With Bayesian Classification Trees},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, Martial Hebert

Abstract: Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not generalize. In this paper, we propose a principled approach to learn shared representations in ConvNets using multi-task learning. Specifically, we propose a new sharing unit: "cross-stitch" unit. These units combine the activations from multiple networks and can be trained end-to-end. A network with cross-stitch units can learn an optimal combination of shared and task-specific representations. Our proposed method generalizes across multiple tasks and shows dramatically improved performance over baseline methods for categories with few training examples.

@InProceedings{Misra_2016_CVPR,author = {Misra, Ishan and Shrivastava, Abhinav and Gupta, Abhinav and Hebert, Martial},title = {Cross-Stitch Networks for Multi-Task Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Deep convolutional neural networks take GPU-days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3x3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64.

@InProceedings{Lavin_2016_CVPR,author = {Lavin, Andrew and Gray, Scott},title = {Fast Algorithms for Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese

Abstract: Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works have shown promising results on discriminatively training the networks to learn semantic feature embeddings where similar examples are mapped close to each other and dissimilar examples are mapped farther apart. In this paper, we describe an algorithm for taking full advantage of the training batches in the neural network training by lifting the vector of pairwise distances within the batch to the matrix of pairwise distances. This step enables the algorithm to learn the state of the art feature embedding by optimizing a novel structured prediction objective for active hard negative mining on the lifted problem. Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning. Our experiments on the CUB-200-2011, CARS196, and Online Products datasets demonstrate significant improvement over existing deep feature embedding methods on all experimented embedding sizes with the GoogLeNet network. The source code and the dataset are available at:  https://github.com/rksltnl/Deep-Metric-Learning-CVPR16

@InProceedings{Song_2016_CVPR,author = {Song, Hyun Oh and Xiang, Yu and Jegelka, Stefanie and Savarese, Silvio},title = {Deep Metric Learning via Lifted Structured Feature Embedding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Ang Li, Dapeng Chen, Yuanliu Liu, Zejian Yuan

Abstract: While great progress has been made in stereo computation over the last decades, large textureless regions remain challenging. Segment-based methods can tackle this problem properly, but their performances are sensitive to the segmentation results. In this paper, we alleviate the sensitivity by generating multiple proposals on absolute and relative disparities from multi-segmentations. These proposals supply rich descriptions of surface structures. Especially, the relative disparity between distant pixels can encode the large structure, which is critical to handle the large texture-less regions. The proposals are coordinated by point-wise competition and pairwise collaboration within a MRF model. During inference, a dynamic programming is performed in different directions with various step sizes, so the long-range connections are better preserved. In the experiments, we carefully analyzed the effectiveness of the major components. Results on the 2014 Middlebury and KITTI 2015 stereo benchmark show that our method is comparable to state-of-the-art.

@InProceedings{Li_2016_CVPR,author = {Li, Ang and Chen, Dapeng and Liu, Yuanliu and Yuan, Zejian},title = {Coordinating Multiple Disparity Proposals for Stereo Computation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Chi Zhang, Zhiwei Li, Rui Cai, Hongyang Chao, Yong Rui

Abstract: In this paper, we propose an RGB-D camera localization approach which takes an effective geometry constraint, i.e. silhouette consistency, into consideration. Unlike existing approaches which usually assume the silhouettes are provided, we consider more practical scenarios and generate the silhouettes for multiple views on the fly. To obtain a set of accurate silhouettes, precise camera poses are required to propagate segmentation cues across views. To perform better localization, accurate silhouettes are needed to constrain camera poses. Therefore the two problems are intertwined with each other and require a joint treatment. Facilitated by the available depth, we introduce a simple but effective silhouette consistency energy term that binds traditional appearance-based multiview segmentation cost and RGB-D frame-to-frame matching cost together. Optimization of the problem w.r.t. binary segmentation masks and camera poses naturally fits in the graph cut minimization framework and the Gauss-Newton non-linear least-squares method respectively. Experiments show that the proposed approach achieves state-of-the-arts performance on both tasks of image segmentation and camera localization.

@InProceedings{Zhang_2016_CVPR,author = {Zhang, Chi and Li, Zhiwei and Cai, Rui and Chao, Hongyang and Rui, Yong},title = {Joint Multiview Segmentation and Localization of RGB-D Images Using Depth-Induced Silhouette Consistency},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox

Abstract: Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks.  Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation.  To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks.  Our datasets are the first large-scale datasets to enable training and evaluation of scene flow methods.  Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.

@InProceedings{Mayer_2016_CVPR,author = {Mayer, Nikolaus and Ilg, Eddy and Hausser, Philip and Fischer, Philipp and Cremers, Daniel and Dosovitskiy, Alexey and Brox, Thomas},title = {A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Rene Ranftl, Vibhav Vineet, Qifeng Chen, Vladlen Koltun

Abstract: We present an approach to dense depth estimation from a single monocular camera that is moving through a dynamic scene. The approach produces a dense depth map from two consecutive frames. Moving objects are reconstructed along with the surrounding environment. We provide a novel motion segmentation algorithm that segments the optical flow field into a set of motion models, each with its own epipolar geometry. We then show that the scene can be reconstructed based on these motion models by optimizing a convex program. The optimization jointly reasons about the scales of different objects and assembles the scene in a common coordinate frame, determined up to a global scale. Experimental results demonstrate that the presented approach outperforms prior methods for monocular depth estimation in dynamic scenes.

@InProceedings{Ranftl_2016_CVPR,author = {Ranftl, Rene and Vineet, Vibhav and Chen, Qifeng and Koltun, Vladlen},title = {Dense Monocular Depth Estimation in Complex Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Wei Feng, Fei-Peng Tian, Qian Zhang, Jizhou Sun

Abstract: Dynamic relocalization of 6D camera pose from single reference image is a costly and challenging task that requires delicate hand-eye calibration and precision positioning platform to do 3D mechanical rotation and translation. In this paper, we show that high-quality camera relocalization can be achieved in a much less expensive way. Based on inexpensive platform with unreliable absolute repositioning accuracy (ARA), we propose a hand-eye calibration free strategy to actively relocate camera into the same 6D pose that produces the input reference image, by sequentially correcting 3D relative rotation and translation. We theoretically prove that, by this strategy, both rotational and translational relative pose can be effectively reduced to zero, with bounded unknown hand-eye pose displacement. To conquer 3D rotation and translation ambiguity, this theoretical strategy is further revised to a practical relocalization algorithm with faster convergence rate and more reliability by jointly adjusting 3D relative rotation and translation. Extensive experiments validate the effectiveness and superior accuracy of the proposed approach on laboratory tests and challenging real-world applications.

@InProceedings{Feng_2016_CVPR,author = {Feng, Wei and Tian, Fei-Peng and Zhang, Qian and Sun, Jizhou},title = {6D Dynamic Camera Relocalization From Single Reference Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Christian Mostegel, Markus Rumpler, Friedrich Fraundorfer, Horst Bischof

Abstract: Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.

@InProceedings{Mostegel_2016_CVPR,author = {Mostegel, Christian and Rumpler, Markus and Fraundorfer, Friedrich and Bischof, Horst},title = {Using Self-Contradiction to Learn Confidence Measures in Stereo Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, Roberto Cipolla

Abstract: Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning have shown promise in this direction but also highlighted the need for enormous quantity of supervised data --- performance increases in proportion to the amount of data used. However, this quickly becomes prohibitive when considering the manual labour needed to collect such data. In this work, we focus our attention on depth based semantic per-pixel labelling as a scene understanding problem and show the potential of computer graphics to generate virtually unlimited labelled data from synthetic 3D scenes. By carefully synthesizing training data with appropriate noise models we show comparable performance to state-of-the-art RGBD systems on NYUv2 dataset despite using only depth data as input and set a benchmark on depth-based segmentation on SUN RGB-D dataset.

@InProceedings{Handa_2016_CVPR,author = {Handa, Ankur and Patraucean, Viorica and Badrinarayanan, Vijay and Stent, Simon and Cipolla, Roberto},title = {Understanding Real World Indoor Scenes With Synthetic Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Hae-Gon Jeon, Joon-Young Lee, Sunghoon Im, Hyowon Ha, In So Kweon

Abstract: Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two cross-spectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods.

@InProceedings{Jeon_2016_CVPR,author = {Jeon, Hae-Gon and Lee, Joon-Young and Im, Sunghoon and Ha, Hyowon and Kweon, In So},title = {Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Gil Ben-Artzi, Yoni Kasten, Shmuel Peleg, Michael Werman

Abstract: Computing the epipolar geometry between cameras with very different viewpoints is often problematic as matching points are hard to find. In these cases, it has been proposed to use information from dynamic objects in the scene for suggesting point and line correspondences.  We propose a speed up of about two orders of magnitude, as well as an increase in robustness and accuracy, to methods computing epipolar geometry from dynamic silhouettes based on a new temporal signature, motion barcode for lines. This is a binary temporal sequence for lines, indicating for each frame the existence of at least one foreground pixel on that line. The motion barcodes of two corresponding epipolar lines are very similar so the search for corresponding epipolar lines can be limited to lines having similar barcodes leading to increased speed, accuracy, and robustness in computing the epipolar geometry.

@InProceedings{Ben-Artzi_2016_CVPR,author = {Ben-Artzi, Gil and Kasten, Yoni and Peleg, Shmuel and Werman, Michael},title = {Camera Calibration From Dynamic Silhouettes Using Motion Barcodes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Structure-From-Motion Revisited

Johannes L. Schonberger, Jan-Michael Frahm

Abstract: Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have tremendously advanced in all regards, robustness, accuracy, completeness, and scalability remain the key problems towards building a truly general-purpose pipeline. We propose a new SfM technique that improves upon the state of the art to make a further step towards this ultimate goal. The full reconstruction pipeline is released to the public as an open-source implementation.

@InProceedings{Schonberger_2016_CVPR,author = {Schonberger, Johannes L. and Frahm, Jan-Michael},title = {Structure-From-Motion Revisited},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: In view selection, little work has been done for optimizing the search process; views must be densely distributed and checked individually. Thus, evaluating poor views wastes much time, and a poor view may even be misidentified as a best one. In this paper, we propose a search strategy by identifying the regions that are very likely to contain best views, referred to as canonical regions. It is by decomposing the model under investigation into meaningful parts, and using the canonical views of these parts to generate canonical regions. Applying existing view selection methods in the canonical regions can not only accelerate the search process but also guarantee the quality of obtained views. As a result, when our canonical regions are used for searching N-best views during comprehensive model analysis, we can attain greater search speed and reduce the number of views required. Experimental results show the effectiveness of our method.

@InProceedings{Wang_2016_CVPR,author = {Wang, Wencheng and Gao, Tianhao},title = {Constructing Canonical Regions for Fast and Effective View Selection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Many non-rigid 3D structures are not modelled well through a low-rank subspace assumption.  This is problematic when it comes to their reconstruction through Structure from Motion (SfM).  We argue in this paper that a more expressive and general assumption can be made around compressible 3D structures.  The vision community, however, has hitherto struggled to formulate effective strategies for recovering such structures after projection without the aid of additional priors (e.g. temporal ordering, rigid substructures, etc.).  In this paper we present a "prior-less" approach to solve compressible SfM.  Specifically, we demonstrate how the problem of SfM - assuming compressible 3D structures - can be theoretically characterized as a block sparse dictionary learning problem.  We validate our approach experimentally by demonstrating reconstructions of 3D structures that are intractable using current state-of-the-art low-rank SfM approaches.

@InProceedings{Kong_2016_CVPR,author = {Kong, Chen and Lucey, Simon},title = {Prior-Less Compressible Structure From Motion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism. In dynamic geometric computer vision applications such as visual SLAM, the so-called rolling shutter effect therefore needs to be properly taken into account. A dedicated relative pose solver appears to be the first problem to solve, as it is of eminent importance to bootstrap any derivation of multi-view geometry. However, despite its significance, it has received inadequate attention to date.  This paper presents a detailed investigation of the geometry of the rolling shutter relative pose problem. We introduce the rolling shutter essential matrix, and establish its link to existing models such as the push-broom cameras, summarized in a clean hierarchy of multi-perspective cameras. The generalization of well-established concepts from epipolar geometry is completed by a definition of the Sampson distance in the rolling shutter case. The work is concluded with a careful investigation of the introduced epipolar geometry for rolling shutter cameras on several dedicated benchmarks.

@InProceedings{Dai_2016_CVPR,author = {Dai, Yuchao and Li, Hongdong and Kneip, Laurent},title = {Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Structure From Motion With Objects

Marco Crocco, Cosimo Rubino, Alessio Del Bue

Abstract: This paper shows for the first time that is possible to reconstruct the position of rigid objects and to jointly recover affine camera calibration solely from a set of object detections in a video sequence. In practice, this work can be considered as the extension of Tomasi and Kanade factorization method using objects. Instead of using points to form a rank constrained measurement matrix, we can form a matrix with similar rank properties using 2D object detection proposals. In detail, we first fit an ellipse onto the image plane at each bounding box as given by the object detector. The collection of all the ellipses in the dual space is used to create a measurement matrix that gives a specific rank constraint. This matrix can be factorised and metrically upgraded in order to provide the affine camera matrices and the 3D position of the objects as an ellipsoid. Moreover, we recover the full 3D quadric thus giving additional information about object occupancy and 3D pose. Finally, we also show that 2D points measurements can be seamlessly included in the framework to reduce the number of objects required. This last aspect unifies the classical point-based Tomasi and Kanade approach with objects in a unique framework. Experiments with synthetic and real data show the feasibility of our approach for the affine camera case.

@InProceedings{Crocco_2016_CVPR,author = {Crocco, Marco and Rubino, Cosimo and Del Bue, Alessio},title = {Structure From Motion With Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: We propose DeepHand to estimate the 3D pose of a hand using depth data from commercial 3D sensors. We discriminatively train convolutional neural networks to output a low dimensional activation feature given a depth map. This activation feature vector is representative of the global or local joint angle parameters of a hand pose. We efficiently identify 'spatial' nearest neighbors to the activation feature, from a database of features corresponding to synthetic depth maps, and store some 'temporal' neighbors from previous frames. Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to to estimate the unknown pose parameters of the input feature vector. Our database of activation features supplements large viewpoint coverage and our hierarchical estimation of pose parameters is robust to occlusions. We show that our approach  compares favorably to state-of-the-art methods while achieving real time performance (32 FPS) on a standard computer.

@InProceedings{Sinha_2016_CVPR,author = {Sinha, Ayan and Choi, Chiho and Ramani, Karthik},title = {DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Zheng Zhang, Chengquan Zhang, Wei Shen, Cong Yao, Wenyu Liu, Xiang Bai

Abstract: In this paper, we propose an unconventional approach for text detection in natural images. Both global and local cues are taken into account for localizing text lines in a coarse-to-fine procedure. First, a Fully Convolutional Network (FCN) model is trained for predicting a salient map of text regions in a holistic manner. Then, a set of hypotheses text lines are estimated by combining the salient map and MSER components. Finally, another FCN classifier is used for predicting the centroid of each character, in order to remove the false hypotheses. The framework is general for handling texts in multiple orientations, languages and fonts. The proposed method consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015, and ICDAR2013.

@InProceedings{Zhang_2016_CVPR,author = {Zhang, Zheng and Zhang, Chengquan and Shen, Wei and Yao, Cong and Liu, Wenyu and Bai, Xiang},title = {Multi-Oriented Text Detection With Fully Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Baoguang Shi, Xinggang Wang, Pengyuan Lyu, Cong Yao, Xiang Bai

Abstract: Recognizing text in natural images is a challenging task with many unsolved problems. Different from those in documents, words in natural images often possess irregular shapes, which are caused by perspective distortion, curved character placement, etc. We propose RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text. RARE is a specially-designed deep neural network, which consists of a Spatial Transformer Network (STN) and a Sequence Recognition Network (SRN). In testing, an image is firstly rectified via a predicted Thin-Plate-Spline (TPS) transformation, into a more "readable" image for the following SRN, which recognizes text through a sequence recognition approach. We show that the model is able to recognize several types of irregular text, including perspective text and curved text. RARE is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems. State-of-the-art or highly-competitive performance achieved on several benchmarks well demonstrates the effectiveness of the proposed model.

@InProceedings{Shi_2016_CVPR,author = {Shi, Baoguang and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},title = {Robust Scene Text Recognition With Automatic Rectification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


George Trigeorgis, Patrick Snape, Mihalis A. Nicolaou, Epameinondas Antonakos, Stefanos Zafeiriou

Abstract: Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem.  Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far.  Specifically, (a) the regressors are learnt independently, (b) the descent directions may cancel one another out and  (c) handcrafted features (e.g., HoGs, SIFT etc.) are mainly used to drive the cascade, which may be sub-optimal for the task at hand. In this paper, we propose a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the aforementioned drawbacks. The recurrent module facilitates the joint optimisation of the regressors by assuming the cascades form a nonlinear dynamical system, in effect fully utilising the information between all cascade levels by introducing a memory unit that shares information across all levels. The convolutional module allows the network to extract features that are specialised for the task at hand and are experimentally shown to outperform hand-crafted features. We show that the application of the proposed architecture for the problem of face alignment results in a strong improvement over the current state-of-the-art.

@InProceedings{Trigeorgis_2016_CVPR,author = {Trigeorgis, George and Snape, Patrick and Nicolaou, Mihalis A. and Antonakos, Epameinondas and Zafeiriou, Stefanos},title = {Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e.g, face recognition and 3D face reconstruction. Recently, there have been a few attempts to solve this problem, but still more research is needed to achieve highly accurate results. In this paper, we propose a face alignment method for large-pose face images, by combining the powerful cascaded CNN regressor method and 3DMM. We formulate the face alignment as a 3DMM fitting problem, where the camera projection matrix and 3D shape parameters are estimated by a cascade of CNN-based regressors. The dense 3D shape allows us to design pose-invariant appearance features for effective CNN learning. Extensive experiments are conducted on the challenging databases (AFLW and AFW), with comparison to the state of the art.

@InProceedings{Jourabloo_2016_CVPR,author = {Jourabloo, Amin and Liu, Xiaoming},title = {Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Given a collection of "in-the-wild" face images captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of an individual along with albedo information. Motivated by the success of recent face reconstruction techniques on large photo collections, we extend prior work to adapt to low quality photo collections with fewer images. We achieve this by fitting a 3D Morphable Model to form a personalized template and developing a novel photometric stereo formulation, under a coarse-to-fine scheme. Superior experimental results are reported on synthetic and real-world photo collections.

@InProceedings{Roth_2016_CVPR,author = {Roth, Joseph and Tong, Yiying and Liu, Xiaoming},title = {Adaptive 3D Face Reconstruction From Unconstrained Photo Collections},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Pavlo Molchanov, Xiaodong Yang, Shalini Gupta, Kihwan Kim, Stephen Tyree, Jan Kautz

Abstract: Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult; 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification; in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three-dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from multi-modal data. We employ connectionist temporal classification to train the network to predict class labels from in-progress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multi-modal dynamic hand gesture dataset captured with depth, color and stereo-IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83.8%, outperforms competing state-of-the-art algorithms, and approaches human accuracy of 88.4%. Moreover, our method achieves state-of-the-art performance on SKIG and ChaLearn2014 benchmarks.

@InProceedings{Molchanov_2016_CVPR,author = {Molchanov, Pavlo and Yang, Xiaodong and Gupta, Shalini and Kim, Kihwan and Tyree, Stephen and Kautz, Jan},title = {Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Hyung Jin Chang, Tobias Fischer, Maxime Petit, Martina Zambelli, Yiannis Demiris

Abstract: In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) a structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) a kinematic correlation measure between pairwise nodes, and (iv) a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on complex articulated synthetic and real data.

@InProceedings{Chang_2016_CVPR,author = {Chang, Hyung Jin and Fischer, Tobias and Petit, Maxime and Zambelli, Martina and Demiris, Yiannis},title = {Kinematic Structure Correspondences via Hypergraph Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Binod Bhattarai, Gaurav Sharma, Frederic Jurie

Abstract: We propose a novel Coupled Projection multi-task Met- ric Learning (CP-mtML) method for large scale face re- trieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more chal- lenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face im- age datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary Pattern (LBP) de- scriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demon- strate the scalability and improved performance of the pro- posed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.

@InProceedings{Bhattarai_2016_CVPR,author = {Bhattarai, Binod and Sharma, Gaurav and Jurie, Frederic},title = {CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: We propose a new pipeline for optical flow computation, based on Deep Learning techniques. We suggest using a Siamese CNN to independently, and in parallel, compute the descriptors of both images. The learned descriptors are then compared efficiently using the L2 norm and do not require network processing of patch pairs. The success of the method is based on an innovative loss function that computes higher moments of the loss distributions for each training batch. Combined with an Approximate Nearest Neighbor patch matching method and a flow interpolation technique, state of the art performance is obtained on the most challenging and competitive optical flow benchmarks.

@InProceedings{Gadot_2016_CVPR,author = {Gadot, David and Wolf, Lior},title = {PatchBatch: A Batch Augmented Loss for Optical Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Tatsunori Taniai, Sudipta N. Sinha, Yoichi Sato

Abstract: We propose a new technique to jointly recover cosegmentation and dense per-pixel correspondence in two images. Our method parameterizes the correspondence field using piecewise similarity transformations and recovers a mapping between the estimated common "foreground" regions in the two images allowing them to be precisely aligned. Our formulation is based on a hierarchical Markov random field model with segmentation and transformation labels. The hierarchical structure uses nested image regions to constrain inference across multiple scales. Unlike prior hierarchical methods which assume that the structure is given, our proposed iterative technique dynamically recovers the structure as a variable along with the labeling. This joint inference is performed in an energy minimization framework using iterated graph cuts. We evaluate our method on a new dataset of 400 image pairs with manually obtained ground truth, where it outperforms state-of-the-art methods designed specifically for either cosegmentation or correspondence estimation.

@InProceedings{Taniai_2016_CVPR,author = {Taniai, Tatsunori and Sinha, Sudipta N. and Sato, Yoichi},title = {Joint Recovery of Dense Correspondence and Cosegmentation in Two Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Yuanlu Xu, Xiaobai Liu, Yang Liu, Song-Chun Zhu

Abstract: This paper presents a hierarchical composition approach for multi-view object tracking. The key idea is to adaptively exploit multiple cues in both 2D and 3D, e.g., ground occupancy consistency, appearance similarity, motion coherence etc., which are mutually complementary while tracking the humans of interests over time. While feature online selection has been extensively studied in the past literature, it remains unclear how to effectively schedule these cues for the tracking purpose especially when encountering various challenges, e.g. occlusions, conjunctions, and appearance variations. To do so, we propose a hierarchical composition model and re-formulate multi-view multi-object tracking as a problem of compositional structure optimization. We setup a set of composition criteria, each of which corresponds to one particular cue. The hierarchical composition process is pursued by exploiting different criteria, which impose constraints between a graph node and its offsprings in the hierarchy. We learn the composition criteria using MLE on annotated data and efficiently construct the hierarchical graph by an iterative greedy pursuit algorithm. In the experiments, we demonstrate superior performance of our approach on three public datasets, one of which is newly created by us to test various challenges in multi-view multi-object tracking.

@InProceedings{Xu_2016_CVPR,author = {Xu, Yuanlu and Liu, Xiaobai and Liu, Yang and Zhu, Song-Chun},title = {Multi-View People Tracking via Hierarchical Trajectory Composition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Jifeng Ning, Jimei Yang, Shaojie Jiang, Lei Zhang, Ming-Hsuan Yang

Abstract: Structured support vector machine (SSVM) based methods has demonstrated encouraging performance in recent object tracking benchmarks. However, the complex and expensive optimization limits their deployment in real-world applications. In this paper, we present a simple yet efficient dual linear SSVM (DLSSVM) algorithm to enable fast learning and execution during tracking. By analyzing the dual variables, we propose a primal classifier update formula where the learning step size is computed in closed form. This online learning method significantly improves the robustness of the proposed linear SSVM with low computational cost. Second, we approximate the intersection kernel for feature representations with an explicit feature map to further improve tracking performance. Finally, we extend the proposed DLSSVM tracker in a multiscale manner to address the "drift" problem. Experimental results on large benchmark datasets with 50 and 100 video sequences show that the proposed DLSSVM tracking algorithm achieves state-of-the-art performance.

@InProceedings{Ning_2016_CVPR,author = {Ning, Jifeng and Yang, Jimei and Jiang, Shaojie and Zhang, Lei and Yang, Ming-Hsuan},title = {Object Tracking via Dual Linear Structured SVM and Explicit Feature Map},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: This paper proposes a novel method for tracking multiple moving objects and recovering their three-dimensional (3D) models separately using multiple calibrated cameras. For robustly tracking objects with similar appearances, the proposed method uses geometric information regarding 3D scene structure rather than appearance. A major limitation of previous techniques is foreground confusion, in which the shapes of objects and/or ghosting artifacts are ignored and are hence not appropriately specified in foreground regions. To overcome this limitation, our method classifies foreground voxels into targets (objects and artifacts) in each frame using a novel, probabilistic two-stage framework. This is accomplished by step-wise application of a track graph describing how targets interact and the maximum a posteriori expectation-maximization algorithm for the estimation of target parameters. We introduce mixture models with semiparametric component distributions regarding 3D target shapes. In order to not confuse artifacts with objects of interest, we automatically detect and track artifacts based on a closed-world assumption. Experimental results show that our method outperforms state-of-the-art trackers on seven public sequences while achieving real-time performance.

@InProceedings{Sekii_2016_CVPR,author = {Sekii, Taiki},title = {Robust, Real-Time 3D Tracking of Multiple Objects With Similar Appearances},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, reporting substantial increase in sales every year. As head-worn cameras do not capture the photographer, it may seem that the anonymity of the photographer is preserved even when the video is publicly distributed.   We show that camera motion, as can be computed from the egocentric video, provides unique identity information. The photographer can be reliably recognized from a few seconds of video captured when walking. The proposed method achieves more than 90% recognition accuracy in cases where the random success rate is only 3%.  Applications can include theft prevention by locking the camera when not worn by its lawful owner. Searching video sharing services (e.g. YouTube) for egocentric videos shot by a specific photographer may also become possible. An important message in this paper is that photographers should be aware that sharing egocentric video will compromise their anonymity, even when their face is not visible.

@InProceedings{Hoshen_2016_CVPR,author = {Hoshen, Yedid and Peleg, Shmuel},title = {An Egocentric Look at Video Photographer Identity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Abstract: We propose a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation. Our network is composed of shared layers and multiple branches of domain-specific layers, where domains correspond to individual training sequences and each branch is responsible for binary classification to identify target in each domain. We train each domain in the network iteratively to obtain generic target representations in the shared layers. When tracking a target in a new sequence, we construct a new network by combining the shared layers in the pretrained CNN with a new binary classification layer, which is updated online. Online tracking is performed by evaluating the candidate windows randomly sampled around the previous target state. The proposed algorithm illustrates outstanding performance in existing tracking benchmarks.

@InProceedings{Nam_2016_CVPR,author = {Nam, Hyeonseob and Han, Bohyung},title = {Learning Multi-Domain Convolutional Neural Networks for Visual Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}


Hedged Deep Tracking

Yuankai Qi, Shengping Zhang, Lei Qin, Hongxun Yao, Qingming Huang, Jongwoo Lim, Ming-Hsuan Yang

Abstract: In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as the features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the last second layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN trackers into a stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared with several state-of-the-art trackers.

@InProceedings{Qi_2016_CVPR,author = {Qi, Yuankai and Zhang, Shengping and Qin, Lei and Yao, Hongxun and Huang, Qingming and Lim, Jongwoo and Yang, Ming-Hsuan},title = {Hedged Deep Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}}