pwc-archive/papers-with-abstracts huggingface dataset:
Superdifferential Cuts for Binary Energies
Authors: Tatsunori Taniai, Yasuyuki Matsushita, Takeshi Naemura
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Binarization, Image Segmentation, Semantic Segmentation

We propose an efficient and general purpose energy optimization method for binary variable energies used in various low-level vision tasks. The proposed method can be used for broad classes of higher-order and pairwise non-submodular functions. We first revisit a submodular-supermodular procedure (SSP) [Narasimhan05], which is previously studied for higher-order energy optimization. We then present our method as generalization of SSP, which is further shown to generalize several state-of-the-art techniques for higher-order and pairwise non-submodular functions [Ayed13, Gorelick14, Tang14]. In the experiments, we apply our method to image segmentation, deconvolution, and binarization, and show improvements over state-of-the-art methods.
The S-Hock Dataset: Analyzing Crowds at the Stadium
Authors: Davide Conigliaro, Paolo Rota, Francesco Setti, Chiara Bassetti, Nicola Conci, Nicu Sebe, Marco Cristani
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Head Pose Estimation, Pose Estimation, Sociology

The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people "interested in watching something specific that they came to see". This can be found at the stadiums, amphitheaters, cinema, etc. In particular, we propose a novel dataset, the Spectators Hockey (S-Hock), which deals with 4 hockey matches during an international tournament. In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc. The labeling focused on the game field also, permitting to relate what is going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard applications as people counting and head pose estimation but also for novel tasks as spectator categorization. For all of these we provide protocols and baseline results, encouraging further research.
Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets
Authors: Wen Wang, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Identification, Face Recognition, Robust classification

This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our goal is to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as Gaussian Mixture Model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. Through these kernels, a weighted Kernel Discriminant Analysis is finally devised which treats the Gaussians in GMMs as samples and their prior probabilities as sample weights. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.
Texture Representations for Image and Video Synthesis
Authors: Georgios Georgiadis, Alessandro Chiuso, Stefano Soatto
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, Texture Synthesis

In texture synthesis and classification, algorithms require a small texture to be provided as an input, which is assumed to be representative of a larger region to be re-synthesized or categorized. We focus on how to characterize such textures and automatically retrieve them. Most works generate these small input textures manually by cropping, which does not ensure maximal compression, nor that the selection is the best representative of the original. We construct a new representation that compactly summarizes a texture, while using less storage, that can be used for texture compression and synthesis. We also demonstrate how the representation can be integrated in our proposed video texture synthesis algorithm to generate novel instances of textures and video hole-filling. Finally, we propose a novel criterion that measures structural and statistical dissimilarity between textures.
Total Variation Regularization of Shape Signals
Authors: Maximilian Baust, Laurent Demaret, Martin Storath, Nassir Navab, Andreas Weinmann
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper introduces the concept of shape signals, i.e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals. The proposed formulation can be seen as the shape-valued generalization of the Rudin-Osher-Fatemi (ROF) functional for intensity images. We derive a variant of the classical finite-dimensional representation of Kendall, but our framework is generic in the sense that it can be combined with any shape space. This representation allows for the explicit computation of geodesics and thus facilitates the efficient numerical treatment of the variational formulation by means of the cyclic proximal point algorithm. Similar to the ROF-functional, we demonstrate experimentally that l_1-type penalties both for data fidelity term and regularizer perform best in regularizing shape signals. Finally, we show applications of our method to shape signals obtained from synthetic, photometric, and medical data sets.
Learning Similarity Metrics for Dynamic Scene Segmentation
Authors: Damien Teney, Matthew Brown, Dmitry Kit, Peter Hall
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Metric Learning, Motion Segmentation, Optical Flow Estimation, Scene Segmentation, Segmentation

This paper addresses the segmentation of videos with arbitrary motion, including dynamic textures, using novel motion features and a supervised learning approach. Dynamic textures are commonplace in natural scenes, and exhibit complex patterns of appearance and motion (e.g. water, smoke, swaying foliage). These are difficult for existing segmentation algorithms, often violate the brightness constancy assumption needed for optical flow, and have complex segment characteristics beyond uniform appearance or motion. Our solution uses custom spatiotemporal filters that capture texture and motion cues, along with a novel metric-learning framework that optimizes this representation for specific objects and scenes. This is used within a hierarchical, graph-based segmentation setting, yielding state-of-the-art results for dynamic texture segmentation. We also demonstrate the applicability of our approach to general object and motion segmentation, showing significant improvements over unsupervised segmentation and results comparable to the best task specific approaches.
Subspace Clustering by Mixture of Gaussian Regression
Authors: Baohua Li, Ying Zhang, Zhouchen Lin, Huchuan Lu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, regression

Subspace clustering is a problem of finding a multisubspace representation that best fits sample points drawn from a high-dimensional space. The existing clustering models generally adopt different norms to describe noise, which is equivalent to assuming that the data are corrupted by specific types of noise. In practice, however, noise is much more complex. So it is inappropriate to simply use a certain norm to model noise. Therefore, we propose Mixture of Gaussian Regression (MoG Regression) for subspace clustering by modeling noise as a Mixture of Gaussians (MoG). The MoG Regression provides an effective way to model a much broader range of noise distributions. As a result, the obtained affinity matrix is better at characterizing the structure of data in real applications. Experimental results on multiple datasets demonstrate that MoG Regression significantly outperforms state-of-the-art subspace clustering methods.
DASC: Dense Adaptive Self-Correlation Descriptor for Multi-Modal and Multi-Spectral Correspondence
Authors: Seungryong Kim, Dongbo Min, Bumsub Ham, Seungchul Ryu, Minh N. Do, Kwanghoon Sohn
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Optical Flow Estimation

Establishing dense visual correspondence between multiple images is a fundamental task in many applications of computer vision and computational photography. Classical approaches, which aim to estimate dense stereo and optical flow fields for images adjacent in viewpoint or in time, have been dramatically advanced in recent studies. However, finding reliable visual correspondence in multi-modal or multi-spectral images still remains unsolved. In this paper, we propose a new dense matching descriptor, called dense adaptive self-correlation (DASC), to effectively address this kind of matching scenarios. Based on the observation that a self-similarity existing within images is less sensitive to modality variations, we define a novel descriptor with a series of an adaptive self-correlation similarity for patches within a local support window. To further improve the matching quality and runtime efficiency, we propose a patch-wise receptive field pooling, in which a sampling pattern is optimized with a discriminative learning. Moreover, the computational redundancy that arises when computing densely sampled descriptor over an entire image is dramatically reduced by applying fast edge-aware filtering. Experiments demonstrate the outstanding performance of the DASC descriptor in many cases of multi-modal and multi-spectral correspondence.
In Defense of Color-Based Model-Free Tracking
Authors: Horst Possegger, Thomas Mauthner, Horst Bischof
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Object Tracking, Video Object Tracking

In this paper, we address the problem of model-free online object tracking based on color representations. According to the findings of recent benchmark evaluations, such trackers often tend to drift towards regions which exhibit a similar appearance compared to the object of interest. To overcome this limitation, we propose an efficient discriminative object model which allows us to identify potentially distracting regions in advance. Furthermore, we exploit this knowledge to adapt the object representation beforehand so that distractors are suppressed and the risk of drifting is significantly reduced. We evaluate our approach on recent online tracking benchmark datasets demonstrating state-of-the-art results. In particular, our approach performs favorably both in terms of accuracy and robustness compared to recent tracking algorithms. Moreover, the proposed approach allows for an efficient implementation to enable online object tracking in real-time.
Best of Both Worlds: Human-Machine Collaboration for Object Annotation
Authors: Olga Russakovsky, Li-Jia Li, Li Fei-Fei
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection

The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.
Robust Multiple Homography Estimation: An Ill-Solved Problem
Authors: Zygmunt L. Szpak, Wojciech Chojnacki, Anton Van Den Hengel
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Homography Estimation

The estimation of multiple homographies between two piecewise planar views of a rigid scene is often assumed to be a solved problem. We show that contrary to popular opinion various crucial aspects of the task have not been adequately emphasised. We are motivated by a growing body of literature in robust multi-structure estimation that purports to solve the multi-homography estimation problem but in fact does not. We demonstrate that the estimation of multiple homographies is an ill-solved problem by deriving new constraints that a set of mutually compatible homographies must satisfy, and by showing that homographies estimated with prevailing methods fail to satisfy the requisite constraints on real-world data. We also explain why incompatible homographies imply inconsistent epipolar geometries. The arguments and experiments presented in this paper signal the need for a new generation of robust multi-structure estimation methods that have the capacity to enforce constraints on projective entities such as homography matrices.
Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition
Authors: Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Domain Adaptation, Object Recognition, Semi-supervised Domain Adaptation

In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation framework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant low-dimensional structures across domains to correct data distribution mismatch and leverages available unlabeled target examples to exploit the underlying intrinsic information in the target domain. Specifically, SDASL conducts the learning by simultaneously minimizing the classification error, preserving the structure within and across domains, and restricting similarity defined on unlabeled target examples. Encouraging results are reported for two challenging domain transfer tasks (including image-to-image and image-to-video transfers) on several standard datasets in the context of both image object recognition and video concept detection.
A Convex Optimization Approach to Robust Fundamental Matrix Estimation
Authors: Yongfang Cheng, Jose A. Lopez, Octavia Camps, Mario Sznaier
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem  can be formulated as a  constrained polynomial optimization, for which a  monotonically convergent sequence of tractable convex relaxations can be obtained by exploiting  recent developments in sparse polynomial optimization. Further, these results allow for deriving conditions certifying that a finite order relaxation has converged to a solution.  A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise,  co-ocurrences, and percentage of outliers. These results are illustrated  with several examples where the proposed algorithm is shown to outperform existing approaches.
Simultaneous Pose and Non-Rigid Shape With Particle Dynamics
Authors: Antonio Agudo, Francesc Moreno-Noguer
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

In this paper, we propose a sequential solution to simultaneously estimate camera pose and non-rigid 3D shape from a monocular video. In contrast to most existing approaches that rely on global representations of the shape, we model the object at a local level, as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency of the estimated shape and camera poses. The resulting approach is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, while it does not require any training data at all. Validation is done in a variety of real video sequences, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our system is shown to perform comparable to competing batch, computationally expensive, methods and shows remarkable improvement with respect to the sequential ones.
Joint Patch and Multi-Label Learning for Facial Action Unit Detection
Authors: Kaili Zhao, Wen-Sheng Chu, Fernando de la Torre, Jeffrey F. Cohn, Honggang Zhang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Unit Detection, Facial Action Unit Detection, Multi-Label Learning

The face is one of the most powerful channel of non-verbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS).  FACS segments the visible effects of facial muscle activation into 30+ action units (AUs).  AUs, which may occur alone and in thousands of combinations, can describe nearly all-possible facial expressions.  Most existing methods for automatic AU detection treat the problem using one-vs-all classifiers and fail to exploit dependencies among AU and facial features.  We introduce joint-patch and multi-label learning (JPML) to address these issues. JPML leverages group sparsity by selecting a sparse subset of facial patches while learning a multi-label classifier. In four of five comparisons on three diverse datasets, CK+, GFT, and BP4D, JPML produced the highest average F1 scores in comparison with state-of-the art.
Real-Time Visual Analysis of Microvascular Blood Flow for Critical Care
Authors: Chao Liu, Hernando Gomez, Srinivasa Narasimhan, Artur Dubrawski, Michael R. Pinsky, Brian Zuckerbraun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Video Stabilization

Microcirculatory monitoring plays an important role in diagnosis and treatment of critical care patients. Sidestream Dark Field (SDF) imaging devices have been used to visualize and support interpretation of the micro-vascular blood flow.  However, due to subsurface scattering within the tissue that embeds the capillaries, transparency of plasma, imaging noise and lack of features, it is difficult to obtain reliable physiological data from SDF videos. Therefore, thus far microcirculatory videos have been analyzed manually with significant input from expert clinicians. In this paper, we present a framework that automates the analysis process. It includes stages of video stabilization, enhancement, and micro-vessel extraction, in order to automatically estimate statistics of the micro blood flows from SDF videos. Our method has been validated in critical care experiments conducted carefully to record the microcirculatory blood flow in test animal subjects before, during and after induced bleeding episodes, as well as to study the effect of fluid resuscitation. Our method is able to extract microcirculatory measurements that are consistent with clinical intuition and it has a potential to become a useful tool in critical care medicine.
JOTS: Joint Online Tracking and Segmentation
Authors: Longyin Wen, Dawei Du, Zhen Lei, Stan Z. Li, Ming-Hsuan Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Segmentation, Video Segmentation, Video Semantic Segmentation

We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. The multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. The multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization
Authors: Jia Xu, Lopamudra Mukherjee, Yin Li, Jamieson Warner, James M. Rehg, Vikas Singh
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Combinatorial Optimization, Common Sense Reasoning, Video Summarization

  With the proliferation of wearable cameras, the number of videos of users documenting their personal lives using   such devices is rapidly increasing. Since such videos may span hours,   there is an important need for mechanisms that represent the information content in a compact form (i.e., shorter videos which are more easily browsable/sharable). Motivated by these applications, this paper focuses on the problem of egocentric video summarization. Such videos are usually continuous with significant camera shake and other quality issues. Because of these reasons, there is growing consensus that direct application of standard video summarization tools to such data yields unsatisfactory performance.   In this paper, we demonstrate that using gaze tracking information (such as fixation and saccade) significantly helps the summarization task.  It allows meaningful comparison of different image frames  and enables deriving personalized   summaries (gaze provides a sense of the camera wearer's intent).   We formulate a summarization model which captures common-sense properties of a good summary, and show that   it can be solved as a submodular function maximization with partition matroid constraints, opening the door to a   rich body of work from combinatorial optimization.   We evaluate our approach on a new gaze-enabled egocentric video dataset (over 15 hours),     which will be a valuable standalone resource.