pwc-archive/papers-with-abstracts huggingface dataset:
Detection Evolution with Multi-order Contextual Co-occurrence
Authors: Guang Chen, Yuanyuan Ding, Jing Xiao, Tony X. Han
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, object-detection, Object Detection

Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-called (1 st -order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector. The statistics of the 1 st -order binary context features are further calculated to construct a high order co-occurrence descriptor. Combining the MOCO feature with the original image feature, we can evolve the baseline object detector to a stronger context aware detector. With the updated detector, we can continue the evolution till the contextual improvements saturate. Using the successful deformable-partmodel detector [13] as the baseline detector, we test the proposed MOCO evolution framework on the PASCAL VOC 2007 dataset [8] and Caltech pedestrian dataset [7]: The proposed MOCO detector outperforms all known state-ofthe-art approaches, contextually boosting deformable part models (ver.5) [13] by 3.3% in mean average precision on the PASCAL 2007 dataset. For the Caltech pedestrian dataset, our method further reduces the log-average miss rate from 48% to 46% and the miss rate at 1 FPPI from 2atfto m44%%compared with the best prior art [6].
Manhattan Scene Understanding via XSlit Imaging
Authors: Jinwei Ye, Yu Ji, Jingyi Yu
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: 3D geometry, Scene Understanding

A Manhattan World (MW) [3] is composed of planar surfaces and parallel lines aligned with three mutually orthogonal principal axes. Traditional MW understanding algorithms rely on geometry priors such as the vanishing points and reference (ground) planes for grouping coplanar structures. In this paper, we present a novel single-image MW reconstruction algorithm from the perspective of nonpinhole cameras. We show that by acquiring the MW using an XSlit camera, we can instantly resolve coplanarity ambiguities. Specifically, we prove that parallel 3D lines map to 2D curves in an XSlit image and they converge at an XSlit Vanishing Point (XVP). In addition, if the lines are coplanar, their curved images will intersect at a second common pixel that we call Coplanar Common Point (CCP). CCP is a unique image feature in XSlit cameras that does not exist in pinholes. We present a comprehensive theory to analyze XVPs and CCPs in a MW scene and study how to recover 3D geometry in a complex MW scene from XVPs and CCPs. Finally, we build a prototype XSlit camera by using two layers of cylindrical lenses. Experimental results on both synthetic and real data show that our new XSlitcamera-based solution provides an effective and reliable solution for MW understanding.
Cumulative Attribute Space for Age and Crowd Density Estimation
Authors: Ke Chen, Shaogang Gong, Tao Xiang, Chen Change Loy
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Age Estimation, Attribute, Crowd Counting, Density Estimation, regression

A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalarvalued output. Such a learning problem is made difficult due to sparse and imbalanced training data and large feature variations caused by both uncertain viewing conditions and intrinsic ambiguities between observable visual features and the scalar values to be estimated. Encouraged by the recent success in using attributes for solving classification problems with sparse training data, this paper introduces a novel cumulative attribute concept for learning a regression model when only sparse and imbalanced data are available. More precisely, low-level visual features extracted from sparse and imbalanced image samples are mapped onto a cumulative attribute space where each dimension has clearly defined semantic interpretation (a label) that captures how the scalar output value (e.g. age, people count) changes continuously and cumulatively. Extensive experiments show that our cumulative attribute framework gains notable advantage on accuracy for both age estimation and crowd counting when compared against conventional regression models, especially when the labelled training data is sparse with imbalanced sampling.
Tensor-Based High-Order Semantic Relation Transfer for Semantic Scene Segmentation
Authors: Heesoo Myeong, Kyoung Mu Lee
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Relation, Scene Segmentation, Segmentation, Semantic Segmentation, Vocal Bursts Intensity Prediction

We propose a novel nonparametric approach for semantic segmentation using high-order semantic relations. Conventional context models mainly focus on learning pairwise relationships between objects. Pairwise relations, however, are not enough to represent high-level contextual knowledge within images. In this paper, we propose semantic relation transfer, a method to transfer high-order semantic relations of objects from annotated images to unlabeled images analogous to label transfer techniques where label information are transferred. We first define semantic tensors representing high-order relations of objects. Semantic relation transfer problem is then formulated as semi-supervised learning using a quadratic objective function of the semantic tensors. By exploiting low-rank property of the semantic tensors and employing Kronecker sum similarity, an efficient approximation algorithm is developed. Based on the predicted high-order semantic relations, we reason semantic segmentation and evaluate the performance on several challenging datasets.
Accurate and Robust Registration of Nonrigid Surface Using Hierarchical Statistical Shape Model
Authors: Hidekata Hontani, Yuto Tsunekawa, Yoshihide Sawada
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

In this paper, we propose a new non-rigid robust registration method that registers a point distribution model (PDM) of a surface to given 3D images. The contributions of the paper are (1) a new hierarchical statistical shape model (SSM) of the surface that has better generalization ability is introduced, (2) the registration algorithm of the hierarchical SSM that can estimate the marginal posterior distribution of the surface location is proposed, and (3) the registration performance is improved by (3-1) robustly registering each local shape of the surface with the sparsity regularization and by (3-2) referring to the appearance between the neighboring model points in the likelihood computation. The SSM of a liver was constructed from a set of clinical CT images, and the performance of the proposed method was evaluated. Experimental results demonstrated that the proposed method outperformed some existing methods that use non-hierarchical SSMs.
Sparse Quantization for Patch Description
Authors: Xavier Boix, Michael Gygli, Gemma Roig, Luc van Gool
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: General Classification, image-classification, Image Classification, Quantization

The representation of local image patches is crucial for the good performance and efficiency of many vision tasks. Patch descriptors have been designed to generalize towards diverse variations, depending on the application, as well as the desired compromise between accuracy and efficiency. We present a novel formulation of patch description, that serves such issues well. Sparse quantization lies at its heart. This allows for efficient encodings, leading to powerful, novel binary descriptors, yet also to the generalization of existing descriptors like SIFT or BRIEF. We demonstrate the capabilities of our formulation for both keypoint matching and image classification. Our binary descriptors achieve state-of-the-art results for two keypoint matching benchmarks, namely those by Brown [6] and Mikolajczyk [18]. For image classification, we propose new descriptors that perform similar to SIFT on Caltech101 [10] and PASCAL VOC07 [9].
What's in a Name? First Names as Facial Attributes
Authors: Huizhong Chen, Andrew C. Gallagher, Bernd Girod
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Age Classification, Attribute

This paper introduces a new idea in describing people using their first names, i.e., the name assigned at birth. We show that describing people in terms of similarity to a vector of possible first names is a powerful description of facial appearance that can be used for face naming and building facial attribute classifiers. We build models for 100 common first names used in the United States and for each pair, construct a pairwise firstname classifier. These classifiers are built using training images downloaded from the internet, with no additional user interaction. This gives our approach important advantages in building practical systems that do not require additional human intervention for labeling. We use the scores from each pairwise name classifier as a set of facial attributes. We show several surprising results. Our name attributes predict the correct first names of test faces at rates far greater than chance. The name attributes are applied to gender recognition and to age classification, outperforming state-of-the-art methods with all training images automatically gathered from the internet.
Context-Aware Modeling and Recognition of Activities in Video
Authors: Yingying Zhu, Nandita M. Nayak, Amit K. Roy-Chowdhury
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Motion Segmentation

In this paper, rather than modeling activities in videos individually, we propose a hierarchical framework that jointly models and recognizes related activities using motion and various context features. This is motivated from the observations that the activities related in space and time rarely occur independently and can serve as the context for each other. Given a video, action segments are automatically detected using motion segmentation based on a nonlinear dynamical model. We aim to merge these segments into activities of interest and generate optimum labels for the activities. Towards this goal, we utilize a structural model in a max-margin framework that jointly models the underlying activities which are related in space and time. The model explicitly learns the duration, motion and context patterns for each activity class, as well as the spatio-temporal relationships for groups of them. The learned model is then used to optimally label the activities in the testing videos using a greedy search method. We show promising results on the VIRAT Ground Dataset demonstrating the benefit of joint modeling and recognizing activities in a wide-area scene.
Learning to Detect Partially Overlapping Instances
Authors: Carlos Arteta, Victor Lempitsky, J. A. Noble, Andrew Zisserman
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: object-detection, Object Detection

The objective of this work is to detect all instances of a class (such as cells or people) in an image. The instances may be partially overlapping and clustered, and hence quite challenging for traditional detectors, which aim at localizing individual instances. Our approach is to propose a set of candidate regions, and then select regions based on optimizing a global classification score, subject to the constraint that the selected regions are non-overlapping. Our novel contribution is to extend standard object detection by introducing separate classes for tuples of objects into the detection process. For example, our detector can pick a region containing two or three object instances, while assigning such region an appropriate label. We show that this formulation can be learned within the structured output SVM framework, and that the inference in such model can be accomplished using dynamic programming on a tree structured region graph. Furthermore, the learning only requires weak annotations a dot on each instance. The improvement resulting from the addition of the capability to detect tuples of objects is demonstrated on quite disparate data sets: fluorescence microscopy images and UCSD pedestrians.
Exemplar-Based Face Parsing
Authors: Brandon M. Smith, Li Zhang, Jonathan Brandt, Zhe Lin, Jianchao Yang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Face Alignment, Face Parsing, Image Segmentation, Scene Parsing, Segmentation, Semantic Segmentation

In this work, we propose an exemplar-based face image segmentation algorithm. We take inspiration from previous works on image parsing for general scenes. Our approach assumes a database of exemplar face images, each of which is associated with a hand-labeled segmentation map. Given a test image, our algorithm first selects a subset of exemplar images from the database, Our algorithm then computes a nonrigid warp for each exemplar image to align it with the test image. Finally, we propagate labels from the exemplar images to the test image in a pixel-wise manner, using trained weights to modulate and combine label maps from different exemplars. We evaluate our method on two challenging datasets and compare with two face parsing algorithms and a general scene parsing algorithm. We also compare our segmentation results with contour-based face alignment results; that is, we first run the alignment algorithms to extract contour points and then derive segments from the contours. Our algorithm compares favorably with all previous works on all datasets evaluated.
Multipath Sparse Coding Using Hierarchical Matching Pursuit
Authors: Liefeng Bo, Xiaofeng Ren, Dieter Fox
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: image-classification, Image Classification

Complex real-world signals, such as images, contain discriminative structures that differ in many aspects including scale, invariance, and data channel. While progress in deep learning shows the importance of learning features through multiple layers, it is equally important to learn features through multiple paths. We propose Multipath Hierarchical Matching Pursuit (M-HMP), a novel feature learning architecture that combines a collection of hierarchical sparse features for image classification to capture multiple aspects of discriminative structures. Our building blocks are MI-KSVD, a codebook learning algorithm that balances the reconstruction error and the mutual incoherence of the codebook, and batch orthogonal matching pursuit (OMP); we apply them recursively at varying layers and scales. The result is a highly discriminative image representation that leads to large improvements to the state-of-the-art on many standard benchmarks, e.g., Caltech-101, Caltech-256, MITScenes, Oxford-IIIT Pet and Caltech-UCSD Bird-200.
Visual Tracking via Locality Sensitive Histograms
Authors: Shengfeng He, Qingxiong Yang, Rynson W. H. Lau, Jiang Wang, Ming-Hsuan Yang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Visual Tracking

This paper presents a novel locality sensitive histogram algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrences of each intensity value by adding ones to the corresponding bin, a locality sensitive histogram is computed at each pixel location and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value declines exponentially with respect to the distance to the pixel location where the histogram is computed; thus every pixel is considered but those that are far away can be neglected due to the very small weights assigned. An efficient algorithm is proposed that enables the locality sensitive histograms to be computed in time linear in the image size and the number of bins. A robust tracking framework based on the locality sensitive histograms is proposed, which consists of two main components: a new feature for tracking that is robust to illumination changes and a novel multi-region tracking algorithm that runs in realtime even with hundreds of regions. Extensive experiments demonstrate that the proposed tracking framework outperforms the state-of-the-art methods in challenging scenarios, especially when the illumination changes dramatically.
Optimized Product Quantization for Approximate Nearest Neighbor Search
Authors: Tiezheng Ge, Kaiming He, Qifa Ke, Jian Sun
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Quantization

Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subspaces that are then quantized separately. Optimal space decomposition is important for the performance of ANN search, but still remains unaddressed. In this paper, we optimize product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks. We present two novel methods for optimization: a nonparametric method that alternatively solves two smaller sub-problems, and a parametric method that is guaranteed to achieve the optimal solution if the input data follows some Gaussian distribution. We show by experiments that our optimized approach substantially improves the accuracy of product quantization for ANN search.
Tracking People and Their Objects
Authors: Tobias Baumgartner, Dennis Mitzel, Bastian Leibe
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object

Current pedestrian tracking approaches ignore important aspects of human behavior. Humans are not moving independently, but they closely interact with their environment, which includes not only other persons, but also different scene objects. Typical everyday scenarios include people moving in groups, pushing child strollers, or pulling luggage. In this paper, we propose a probabilistic approach for classifying such person-object interactions, associating objects to persons, and predicting how the interaction will most likely continue. Our approach relies on stereo depth information in order to track all scene objects in 3D, while simultaneously building up their 3D shape models. These models and their relative spatial arrangement are then fed into a probabilistic graphical model which jointly infers pairwise interactions and object classes. The inferred interactions can then be used to support tracking by recovering lost object tracks. We evaluate our approach on a novel dataset containing more than 15,000 frames of personobject interactions in 325 video sequences and demonstrate good performance in challenging real-world scenarios.
Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow
Authors: Asad A. Butt, Robert T. Collins
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We propose a method for global multi-target tracking that can incorporate higher-order track smoothness constraints such as constant velocity. Our problem formulation readily lends itself to path estimation in a trellis graph, but unlike previous methods, each node in our network represents a candidate pair of matching observations between consecutive frames. Extra constraints on binary flow variables in the graph result in a problem that can no longer be solved by min-cost network flow. We therefore propose an iterative solution method that relaxes these extra constraints using Lagrangian relaxation, resulting in a series of problems that ARE solvable by min-cost flow, and that progressively improve towards a high-quality solution to our original optimization problem. We present experimental results showing that our method outperforms the standard network-flow formulation as well as other recent algorithms that attempt to incorporate higher-order smoothness constraints.
In Defense of 3D-Label Stereo
Authors: Carl Olsson, Johannes Ulen, Yuri Boykov
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

It is commonly believed that higher order smoothness should be modeled using higher order interactions. For example, 2nd order derivatives for deformable (active) contours are represented by triple cliques. Similarly, the 2nd order regularization methods in stereo predominantly use MRF models with scalar (1D) disparity labels and triple clique interactions. In this paper we advocate a largely overlooked alternative approach to stereo where 2nd order surface smoothness is represented by pairwise interactions with 3D-labels, e.g. tangent planes. This general paradigm has been criticized due to perceived computational complexity of optimization in higher-dimensional label space. Contrary to popular beliefs, we demonstrate that representing 2nd order surface smoothness with 3D labels leads to simpler optimization problems with (nearly) submodular pairwise interactions. Our theoretical and experimental results demonstrate advantages over state-of-the-art methods for 2nd order smoothness stereo. 1
Compressible Motion Fields
Authors: Giuseppe Ottaviano, Pushmeet Kohli
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Motion Compensation, Optical Flow Estimation, Video Compression

Traditional video compression methods obtain a compact representation for image frames by computing coarse motion fields defined on patches of pixels called blocks, in order to compensate for the motion in the scene across frames. This piecewise constant approximation makes the motion field efficiently encodable, but it introduces block artifacts in the warped image frame. In this paper, we address the problem of estimating dense motion fields that, while accurately predicting one frame from a given reference frame by warping it with the field, are also compressible. We introduce a representation for motion fields based on wavelet bases, and approximate the compressibility of their coefficients with a piecewise smooth surrogate function that yields an objective function similar to classical optical flow formulations. We then show how to quantize and encode such coefficients with adaptive precision. We demonstrate the effectiveness of our approach by comparing its performance with a state-of-the-art wavelet video encoder. Experimental results on a number of standard flow and video datasets reveal that our method significantly outperforms both block-based and optical-flow-based motion compensation algorithms.