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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Compact Bilinear pooling

Compact Bilinear pooling

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Gao, Yang Beijbom, Oscar Zhang, Ning Darrell, Trevor EECS UC Berkeley United States Snapchat Inc. United States

ISBN: (纸本)9781467388511

Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.

关键词： Face recognition

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Learning compact binary descriptors with unsupervised deep neural networks

Learning compact binary descriptors with unsupervised deep n...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Lin, Kevin Lu, Jiwen Chen, Chu-Song Zhou, Jie Institute of Information Science Academia Sinica Taipei Taiwan Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781467388511

In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for efficient visual object matching. Unlike most existing binary descriptors which were designed with random projections or linear hash functions, we develop a deep neural network to learn binary descriptors in an unsupervised manner. We enforce three criterions on binary codes which are learned at the top layer of our network:1) minimal loss quantization, 2) evenly distributed codes and 3) uncorrelated bits. Then, we learn the parameters of the networks with a back-propagation technique. Experimental results on three different visual analysis tasks including image matching, image retrieval, and object recognition clearly demonstrate the effectiveness of the proposed approach.

关键词： pattern recognition

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Recognizing car fluents from video

Recognizing car fluents from video

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Li, Bo Wu, Tianfu Xiong, Caiming Zhu, Song-Chun Beijing Lab of Intelligent Information Technology Beijing Institute of Technology China Department of Statistics University of California Los Angeles United States Metamind Inc. United States

ISBN: (纸本)9781467388511

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.

关键词： pattern recognition

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Information-driven adaptive structured-light scanners

Information-driven adaptive structured-light scanners

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Rosman, Guy Rus, Daniela Fisher, John W. CSAIL Massachusetts Institute of Technology United States

ISBN: (纸本)9781467388511

Sensor planning and active sensing, long studied in robotics, adapt sensor parameters to maximize a utility function while constraining resource expenditures. Here we consider information gain as the utility function. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles can be used as part of the 3D sensor. We describe the relevant generative model for structured-light 3D scanning and show how adaptive pattern selection can maximize information gain in an open-loop-feedback manner. We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion. We show results for both subproblems with several pattern dictionary choices and demonstrate their usefulness for pose estimation and depth acquisition.

关键词： computer vision

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Equiangular kernel dictionary learning with applications to dynamic texture analysis

Equiangular kernel dictionary learning with applications to ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Quan, Yuhui Bao, Chenglong Ji, Hui School of Computer Science and Engineering South China Univ. of Tech. Guangzhou510006 China Department of Mathematics National University of Singapore Singapore117542 Singapore

ISBN: (纸本)9781467388511

Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e.g. dynamic texture (DT). Such nonlinear properties can be exploited by the so-called kernel sparse coding. This paper proposed an equiangular kernel dictionary learning method with optimal mutual coherence to exploit the nonlinear sparsity of high-dimensional visual data. Two main issues are addressed in the proposed method: (1) coding stability for redundant dictionary of infinite-dimensional space; and (2) computational efficiency for computing kernel matrix of training samples of high-dimensional data. The proposed kernel sparse coding method is applied to dynamic texture analysis with both local DT pattern extraction and global DT pattern characterization. The experimental results showed its performance gain over existing methods.

关键词： pattern recognition

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Guaranteed outlier removal with mixed integer linear programs

Guaranteed outlier removal with mixed integer linear program...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Chin, Tat-Jun Kee, Yang Heng Eriksson, Anders Neumann, Frank School of Computer Science University of Adelaide Australia School of Electrical Engineering and Computer Science Queensland University of Technology Australia

ISBN: (纸本)9781467388511

The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed. Towards the goal of solving maximum consensus exactly, we present guaranteed outlier removal as a technique to reduce the runtime of exact algorithms. Specifically, before conducting global optimization, we attempt to remove data that are provably true outliers, i.e., those that do not exist in the maximum consensus set. We propose an algorithm based on mixed integer linear programming to perform the removal. The result of our algorithm is a smaller data instance that admits a much faster solution by subsequent exact algorithms, while yielding the same globally optimal result as the original problem. We demonstrate that overall speedups of up to 80% can be achieved on common vision problems1.

关键词： Integer programming

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A hierarchical deep temporal model for group activity recognition

A hierarchical deep temporal model for group activity recogn...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ibrahim, Mostafa S. Muralidharan, Srikanth Deng, Zhiwei Vahdat, Arash Mori, Greg School of Computing Science Simon Fraser University Burnaby Canada

ISBN: (纸本)9781467388511

In group activity recognition, the temporal dynamics of the whole activity can be inferred based on the dynamics of the individual people representing the activity. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. To make use of these observations, we present a 2-stage deep temporal model for the group activity recognition problem. In our model, a LSTM model is designed to represent action dynamics of individual people in a sequence and another LSTM model is designed to aggregate person-level information for whole activity understanding. We evaluate our model over two datasets: the Collective Activity Dataset and a new volleyball dataset. Experimental results demonstrate that our proposed model improves group activity recognition performance compared to baseline methods.

关键词： Long short-term memory

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Context-aware Gaussian fields for non-rigid point set registration

Context-aware Gaussian fields for non-rigid point set regist...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Gang Wang, Zhicheng Chen, Yufei Zhou, Qiangqiang Zhao, Weidong CAD Research Center College of Electronics and Information Engineering Tongji University Shanghai201804 China

ISBN: (纸本)9781467388511

Point set registration (PSR) is a fundamental problem in computer vision and pattern recognition, and it has been successfully applied to many applications. Although widely used, existing PSR methods cannot align point sets robustly under degradations, such as deformation, noise, occlusion, outlier, rotation, and multi-view changes. This paper proposes context-aware Gaussian fields (CA-LapGF) for nonrigid PSR subject to global rigid and local non-rigid geometric constraints, where a laplacian regularized term is added to preserve the intrinsic geometry of the transformed set. CA-LapGF uses a robust objective function and the quasi-Newton algorithm to estimate the likely correspondences, and the non-rigid transformation parameters between two point sets iteratively. The CA-LapGF can estimate non-rigid transformations, which are mapped to reproducing kernel Hilbert spaces, accurately and robustly in the presence of degradations. Experimental results on synthetic and real images reveal that how CA-LapGF outperforms state-of-the-art algorithms for non-rigid PSR.

关键词： pattern recognition

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Actor-action semantic segmentation with grouping process models

Actor-action semantic segmentation with grouping process mod...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Xu, Chenliang Corso, Jason J. Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

ISBN: (纸本)9781467388511

Actor-action semantic segmentation made an important step toward advanced video understanding: what action is happening; who is performing the action; and where is the action happening in space-time. Current methods based on layered CRFs for this problem are local and unable to capture the long-ranging interactions of video parts. We propose a new model that combines the labeling CRF with a supervoxel hierarchy, where supervoxels at various scales provide cues for possible groupings of nodes in the CRF to encourage adaptive and long-ranging interactions. The new model defines a dynamic and continuous process of information exchange: the CRF influences what supervoxels in the hierarchy are active, and these active supervoxels, in turn, affect the connectivities in the CRF; we hence call it a grouping process model. By further incorporating the video-level recognition, the proposed method achieves a large margin of 60% relative improvement over the state of the art on the recent A2D large-scale video labeling dataset, which demonstrates the effectiveness of our modeling.

关键词： computer vision

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Direct prediction of 3D body poses from motion compensated sequences

Direct prediction of 3D body poses from motion compensated s...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Tekin, Bugra Rozantsev, Artem Lepetit, Vincent Fua, Pascal CVLab EPFL Lausanne Switzerland TU Graz Graz Austria

ISBN: (纸本)9781467388511

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Previous approaches typically compute candidate poses in individual frames and then link them in a post-processing step to resolve ambiguities. By contrast, we directly regress from a spatio-temporal volume of bounding boxes to a 3D pose in the central frame. We further show that, for this approach to achieve its full potential, it is essential to compensate for the motion in consecutive frames so that the subject remains centered. This then allows us to effectively overcome ambiguities and improve upon the state-of-the-art by a large margin on the Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation benchmarks.

关键词： computer vision

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