pwc-archive/papers-with-abstracts huggingface dataset:
Combination Features and Models for Human Detection
Authors: Yunsheng Jiang, Jinwen Ma
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Human Detection

This paper presents effective combination models with certain combination features for human detection. In the past several years, many existing features/models have achieved impressive progress, but their performances are still limited by the biases rooted in their self-structures, that is, a particular kind of feature/model may work well for some types of human bodies, but not for all the types. To tackle this difficult problem, we combine certain complementary features/models together with effective organization/fusion methods. Specifically, the HOG features, color features and bar-shape features are combined together with a cell-based histogram structure to form the so-called HOG-III features. Moreover, the detections from different models are fused together with the new proposed weighted-NMS algorithm, which enhances the probable "true" activations as well as suppresses the overlapped detections. The experiments on PASCAL VOC datasets demonstrate that, both the HOG-III features and the weighted-NMS fusion algorithm are effective (obvious improvement for detection performance) and efficient (relatively less computation cost): When applied to human detection task with the Grammar model and Poselet model, they can boost the detection performance significantly; Also, when extended to detection of the whole VOC 20 object categories with the deformable part-based model and deepCNN-based model, they still show competitive improvements.
A Metric Parametrization for Trifocal Tensors With Non-Colinear Pinholes
Authors: Spyridon Leonardos, Roberto Tron, Kostas Daniilidis
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

The trifocal tensor, which describes the relation between projections of points and lines in three views, is a fundamental entity of geometric computer vision. In this work, we investigate a new parametrization of the trifocal tensor for calibrated cameras with non-colinear pinholes obtained from a quotient Riemannian manifold.  We incorporate this formulation into state-of-the art methods for optimization on manifolds, and show, through experiments in pose averaging, that it produces a meaningful way to measure distances between trifocal tensors.
An Efficient Volumetric Framework for Shape Tracking
Authors: Benjamin Allain, Jean-Sebastien Franco, Edmond Boyer
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Temporal Sequences

Recovering 3D shape motion using visual information is an important problem with many applications in computer vision and computer graphics, among other domains.  Most existing approaches rely on surface-based strategies, where surface models are fit to visual surface observations. While numerically plausible, this paradigm ignores the fact that the observed surfaces often delimit volumetric shapes, for which deformations are constrained by the volume inside the shape. Consequently, surface-based strategies can fail when the observations define several feasible surfaces, whereas volumetric considerations are more restrictive with respect to the admissible solutions. In this work, we investigate a novel volumetric shape parametrization to track shapes over temporal sequences. In constrast to Eulerian grid discretizations of the observation space, such as voxels, we consider general shape tesselations yielding more convenient cell decompositions, in particular the Centroidal Voronoi Tesselation. With this shape representation, we devise a tracking method that exploits volumetric information, both for the data term evaluating observation conformity, and for expressing deformation constraints that enforce prior assumptions on motion. Experiments on several datasets demonstrate similar or improved precisions over state-of-the-art methods, as well as improved robustness, a critical issue when tracking sequentially over time frames.
Structured Sparse Subspace Clustering: A Unified Optimization Framework
Authors: Chun-Guang Li, Rene Vidal
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Motion Segmentation, Segmentation

Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State of the art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to state of the art results in many applications, it is sub-optimal because it does not exploit the fact that the affinity and the segmentation depend on each other. In this paper, we propose a unified optimization framework for learning both the affinity and the segmentation. Our framework is based on expressing each data point as a structured sparse linear combination of all other data points, where the structure is induced by a norm that depends on the unknown segmentation. We show that both the segmentation and the structured sparse representation can be found via a combination of an alternating direction method of multipliers with spectral clustering. Experiments on a synthetic data, the Hopkins 155 motion segmentation database, and the Extended Yale B data set demonstrate the effectiveness of our approach.
Delving Into Egocentric Actions
Authors: Yin Li, Zhefan Ye, James M. Rehg
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Temporal Action Localization

We address the challenging problem of recognizing the camera wearer's actions from videos captured by an egocentric camera. Egocentric videos encode a rich set of signals regarding the camera wearer, including head movement, hand pose and gaze information. We propose to utilize these mid-level egocentric cues for egocentric action recognition. We present a novel set of egocentric features and show how they can be combined with motion and object features. The result is a compact representation with superior performance. In addition, we provide the first systematic evaluation of motion, object and egocentric cues in egocentric action recognition. Our benchmark leads to several surprising findings. These findings uncover the best practices for egocentric actions, with a significant performance boost over all previous state-of-the-art methods on three publicly available datasets.
Latent Trees for Estimating Intensity of Facial Action Units
Authors: Sebastian Kaltwang, Sinisa Todorovic, Maja Pantic
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper is about estimating intensity levels of Facial Action Units (FAUs) in videos as an important and challenging step toward interpreting facial expressions. To address uncertainty in detections of facial landmark points, used as out input features, we formulate a new generative framework comprised of a graphical model, inference, and algorithms for learning both model parameters and structure. Our model is a latent tree (LT) that represents  input features of facial landmark points and FAU intensities as leaf nodes, and encodes their higher-order dependencies with latent nodes at tree levels closer to the root. No other restrictions are placed on the model structure beyond that it is a tree. We specify a new algorithm for efficient learning of model structure that iteratively builds LT by adding either new edge or new hidden node to LT, whichever of these two graph-edit operations gives the highest increase of the joint likelihood. Our structure learning efficiently computes likelihood increase and selects an optimal graph revision without considering all possible structural changes. For FAU intensity estimation, we derive closed-form expressions of posterior marginals of all variables in LT, and specify an efficient inference of in two passes -- bottom-up and top-down. Our evaluation on the benchmark DISFA and ShoulderPain datasets, in subject-independent setting, demonstrate that we outperform the state of the art, even in the presence of significant noise in locations of facial landmark points. We demonstrate our correct learning of model structure by probabilistically sampling facial landmark points, conditioned on a given FAU intensity, and thus generating plausible facial expressions.
Robust Regression on Image Manifolds for Ordered Label Denoising
Authors: Hui Wu, Richard Souvenir
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Denoising, regression

In this paper, we present a computationally efficient and non-parametric method for robust regression on manifolds. We apply our algorithm to the problem of correcting mislabeled examples from image collections with ordered (e.g., real-valued, ordinal) labels. Compared to related methods for robust regression, our method achieves superior denoising accuracy on a variety of data sets, with label corruption levels as high as 80%. For a diverse set of widely-used, large-scale, publicly-available data sets, our approach results in image labels that more accurately describe the associated images.
Privacy Preserving Optics for Miniature Vision Sensors
Authors: Francesco Pittaluga, Sanjeev J. Koppal
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Recognition, Privacy Preserving

The next wave of micro and nano devices will create a world with trillions of small networked cameras. This will lead to increased concerns about privacy and security. Most privacy preserving algorithms for computer vision are applied after image/video data has been captured. We propose to use privacy preserving optics that filter or block sensitive information directly from the incident light-field before sensor measurements are made, adding a new layer of privacy. In addition to balancing the privacy and utility of the captured data, we address trade-offs unique to miniature vision sensors, such as achieving high-quality field-of-view and resolution within the constraints of mass and volume. Our privacy preserving optics enable applications such as depth sensing, full-body motion tracking, people counting, blob detection and privacy preserving face recognition. While we demonstrate applications on macro-scale devices (smartphones, webcams, etc.) our theory has impact for smaller devices.
Deep Transfer Metric Learning
Authors: Junlin Hu, Jiwen Lu, Yap-Peng Tan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Verification, Metric Learning, Person Re-Identification

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML where the output of both the hidden layers and the top layer are optimized jointly. Experimental results on cross-dataset face verification and person re-identification validate the effectiveness of the proposed methods.
DynamicFusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time
Authors: Richard A. Newcombe, Dieter Fox, Steven M. Seitz
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We present the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our DynamicFusion approach reconstructs scene geometry whilst simultaneously estimating a dense volumetric 6D motion field that warps the estimated geometry into a live frame. Like KinectFusion, our system produces increasingly denoised, detailed, and complete reconstructions as more measurements are fused, and displays the updated model in real time. Because we do not require a template or other prior scene model, the approach is applicable to a wide range of moving objects and scenes.
Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches
Authors: Yang Li, Jianke Zhu, Steven C. H. Hoi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Visual Tracking

Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that can be tracked effectively through the whole tracking process. Specifically, we present a tracking reliability metric to measure how reliably a patch can be tracked, where a probability model is proposed to estimate the distribution of reliable patches under a sequential Monte Carlo framework. As the reliable patches distributed over the image, we exploit the motion trajectories to distinguish them from the background. Therefore, the visual object can be defined as the clustering of homo-trajectory patches, where a Hough voting-like scheme is employed to estimate the target state. Encouraging experimental results on a large set of sequences showed that the proposed approach is very effective and  in comparison to the state-of-the-art trackers. The full source code of our implementation will be publicly available.
Predicting Eye Fixations Using Convolutional Neural Networks
Authors: Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

It is believed that eye movements in free-viewing of natural scenes are directed by both bottom-up visual saliency and top-down visual factors. In this paper, we propose a novel computational framework to simultaneously learn these two types of visual features from raw image data using a multiresolution convolutional neural network (Mr-CNN) for predicting eye fixations. The Mr-CNN is directly trained from image regions centered on fixation and non-fixation locations over multiple resolutions, using raw image pixels as inputs and eye fixation attributes as labels. Diverse top-down visual features can be learned in higher layers. Meanwhile bottom-up visual saliency can also be inferred via combining information over multiple resolutions. Finally, optimal integration of bottom-up and top-down cues can be learned in the last logistic regression layer to predict eye fixations. The proposed approach achieves state-of-the-art results over four publically available benchmark datasets, demonstrating the superiority of our work.
Kernel Fusion for Better Image Deblurring
Authors: Long Mai, Feng Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring, Image Deblurring

Kernel estimation for image deblurring is a challenging task and a large number of algorithms have been developed. Our hypothesis is that while individual kernels estimated using different methods alone are sometimes inadequate, they often complement each other. This paper addresses the problem of fusing multiple kernels  estimated using different methods into a more accurate one that can better support image deblurring than each individual kernel. In this paper, we develop a data-driven approach to kernel fusion that learns how each kernel contributes to the final kernel and how they interact with each other. We discuss various kernel fusion models and find that kernel fusion using Gaussian Conditional Random Fields performs best. This Gaussian Conditional Random Fields-based kernel fusion method not only models how individual kernels are fused at each kernel element but also the interaction of kernel fusion among multiple kernel elements. Our experiments show that our method can significantly improve image deblurring by combining kernels from multiple methods into a better one.
Direction Matters: Depth Estimation With a Surface Normal Classifier
Authors: Christian Hane, Lubor Ladicky, Marc Pollefeys
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Depth Estimation, Stereo Matching, Stereo Matching Hand

In this work we make use of recent advances in data driven classification to improve standard approaches for binocular stereo matching and single view depth estimation. Surface normal direction estimation has become feasible and shown to work reliably on state of the art benchmark datasets. Information about the surface orientation contributes crucial information about the scene geometry in cases where standard approaches struggle. We describe, how the responses of such a classifier can be included in global stereo matching approaches. One of the strengths of our approach is, that we can use the classifier responses for a whole set of directions and let the final optimization decide about the surface orientation. This is important in cases where based on the classifier, multiple different surface orientations seem likely. We evaluate our method on two challenging real-world datasets for the two proposed applications. For the binocular stereo matching we use road scene imagery taken from a car and for the single view depth estimation we use images taken in indoor environments.
Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection
Authors: George Papandreou, Iasonas Kokkinos, Pierre-Andre Savalle
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, image-classification, Image Classification, Multiple Instance Learning, object-detection, Object Detection, Position, Translation

Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple 'max-pooling' (MP) layers. In this work we show that alternative methods of modeling deformations can improve the accuracy and efficiency of DCNNs. First, we introduce epitomic convolution as an alternative to the common convolution-MP cascade of DCNNs, that comes with the same computational cost but favorable learning properties. Second, we introduce a Multiple Instance Learning algorithm to accommodate global translation and scaling in image classification, yielding an efficient algorithm that trains and tests a DCNN in a consistent manner. Third we develop a DCNN sliding window detector that explicitly, but efficiently, searches over the object's position, scale, and aspect ratio. We provide competitive image classification and localization results on the ImageNet dataset and object detection results on Pascal VOC2007.
Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision
Authors: Yezhou Yang, Cornelia Fermuller, Yi Li, Yiannis Aloimonos
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Segmentation, Action Understanding, General Classification, Vocal Bursts Type Prediction

The grasp type provides crucial information about human action. However, recognizing the grasp type in unconstrained scenes is challenging because of the large variations in appearance, occlusions and  geometric distortions. In this paper, first we present a convolutional neural network to classify functional hand grasp types. Experiments on a public static scene hand data set validate good performance of the presented method. Then we present two applications utilizing grasp type classification: (a) inference of human action intention and (b) fine level manipulation action segmentation. Experiments on both tasks demonstrate the usefulness of grasp type as a cognitive feature for computer vision. This study shows that the grasp type is a powerful symbolic representation for action understanding, and thus opens new avenues for future research.
Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging
Authors: Johannes Hofmanninger, Georg Langs
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Content based image retrieval is highly relevant in medical imaging, since it makes vast amounts of imaging data accessible for comparison during diagnosis. Finding image similarity measures that reflect diagnostically relevant relationships is challenging, since the overall appearance variability is high compared to often subtle signatures of diseases. To learn models that capture the relationship between semantic clinical information and image elements at scale, we have to rely on data generated during clinical routine (images and radiology reports), since expert annotation is prohibitively costly. Here we show that re-mapping visual features extracted from medical imaging data based on weak labels that can be found in corresponding radiology reports creates descriptions of local image content capturing clinically relevant information. We show that these semantic profiles enable higher recall and precision during retrieval compared to visual features, and that we can even map semantic terms describing clinical findings from radiology reports to localized image volume areas.
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision
Authors: Stephan Schraml, Ahmed Nabil Belbachir, Horst Bischof
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Stereo Matching, Stereo Matching Hand

This paper presents a stereo matching approach for a novel multi-perspective panoramic stereo vision system, making use of asynchronous and non-simultaneous stereo imaging towards real-time 3D 360deg vision. The method is designed for events representing the scenes visual contrast as a sparse visual code allowing the stereo reconstruction of high resolution panoramic views. We propose a novel cost measure for the stereo matching, which makes use of a similarity measure based on event distributions. Thus, the robustness to variations in event occurrences was increased. An evaluation of the proposed stereo method is presented using distance estimation of panoramic stereo views and ground truth data. Furthermore, our approach is compared to standard stereo methods applied on event-data. Results show that we obtain 3D reconstructions of 1024 x 3600 round views and outperform depth reconstruction accuracy of state-of-the-art methods on event data.
Graph-Based Simplex Method for Pairwise Energy Minimization With Binary Variables
Authors: Daniel Prusa
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

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