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Robust Pedestrian detection for semi-automatic construction of a crowded person re-identification dataset 1

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10th International conference on Articulated Motion and Deformable Objects, AMDO 2018

作者： Huang, Zengxi Feng, Zhen-Hua Yan, Fei Kittler, Josef Wu, Xiao-Jun School of Computer and Software Engineering Xihua University Chengdu China Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Machine Intelligence Jiangnan University Wuxi China

ISBN: (数字)9783319945446

ISBN: (纸本)9783319945439

the problem of re-identification of people in a crowd commonly arises in real application scenarios, yet it has received less attention than it deserves. To facilitate research focusing on this problem, we have embarked on constructing a new person re-identification dataset with many instances of crowded indoor and outdoor scenes. this paper proposes a two-stage robust method for pedestrian detection in a complex crowded background to provide bounding box annotations. the first stage is to generate pedestrian proposals using Faster R-CNN and locate each pedestrian using Non-maximum Suppression (NMS). Candidates in dense proposal regions are merged to identify crowd patches. We then apply a bottom-up human pose estimation method to detect individual pedestrians in the crowd patches. the locations of all subjects are achieved based on the bounding boxes from the two stages. the identity of the detected subjects throughout each video is then automatically annotated using multiple features and spatial-temporal clues. the experimental results on a crowded pedestrians dataset demonstrate the effectiveness and efficiency of the proposed method. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Gesture recognition

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Effective Learning with Joint Discriminative and Representative Feature Selection 18th

Effective Learning with Joint Discriminative and Representat...

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18th International conference on Computational Science (ICCS)

作者： Wang, Shupeng Zhang, Xiao-Yu Dang, Xianglei Li, Binbin Wang, Haiping Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783319937137;9783319937120

Feature selection plays an important role in various machine learning tasks such as classification. In this paper, we focus on both discriminative and representative abilities of the features, and propose a novel feature selection method with joint exploration on both labeled and unlabeled data. In particular, we implement discriminative feature selection to extract the features that can best reveal the underlying classification labels, and develop representative feature selection to obtain the features with optimal self-expressive performance. Both methods are formulated as joint l(2),(1)-norm minimization problems. An effective alternate minimization algorithm is also introduced with analytic solutions in a column-by-column manner. Extensive experiments on various classification tasks demonstrate the advantage of the proposed method over several state-of-the-art methods.

关键词： Feature selection Discriminative feature Representative feature Matrix optimization Model learning

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A Message Passing Algorithm for the Minimum Cost Multicut Problem 30

A Message Passing Algorithm for the Minimum Cost Multicut Pr...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Swoboda, Paul Andres, Bjoern IST Austria Klosterneuburg Austria MPI Informat Saarbrucken Germany

ISBN: (纸本)9781538604571

We propose a dual decomposition and linear program relaxation of the NP-hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral relaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. this algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining.

关键词： Wheels Software algorithms computer vision Image segmentation Message passing Optimization Algorithm design and analysis message passing Algorithm design and analysis Software algorithms images computer vision Wheels image segmentation multicut linear programming Commercial software

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Learning Adaptive Receptive Fields for Deep Image Parsing Network 30

Learning Adaptive Receptive Fields for Deep Image Parsing Ne...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wei, Zhen Sun, Yao Wang, Jinqiao Lai, Hanjiang Liu, Si Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur SKLOIS Beijing 100093 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510275 Guangdong Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China

ISBN: (纸本)9781538604571

In this paper, we introduce a novel approach to regulate receptive field in deep image parsing network automatically. Unlike previous works which have stressed much importance on obtaining better receptive fields using manually selected dilated convolutional kernels, our approach uses two affine transformation layers in the network's backbone and operates on feature maps. Feature maps will be inflated/shrinked by the new layer and therefore receptive fields in following layers are changed accordingly. By end-to-end training, the whole framework is data-driven without laborious manual intervention. the proposed method is generic across dataset and different tasks. We conduct extensive experiments on both general image parsing task and face parsing task as concrete examples to demonstrate the method's superior regulation ability over manual designs.

关键词： Kernel Interpolation Training Manuals Face

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Image and Graphics Technologies and Applications 1st ed. 2018

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丛书名： Communications in computer and Information Science

2018年

作者： Yongtian Wang Zhiguo Jiang Yuxin Peng

ISBN: (数字)9789811317026

ISBN: (纸本)9789811317019

this book constitutes the refereed proceedings of the 13th chinese conference on Image and Graphics Technologies and Applications, IGTA 2018, held in Beijing, China in April, 2018. the 64papers presented were carefully reviewed and selected from 138submissions. they provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR; as well as image technology applications.

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Pyramid Scene Parsing Network 30

Pyramid Scene Parsing Network

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Hengshuang Shi, Jianping Qi, Xiaojuan Wang, Xiaogang Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China SenseTime Grp Ltd Beijing Peoples R China

ISBN: (纸本)9781538604571

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction. the proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields the new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.

关键词： Pyramid Scene Parsing Network PSPNet PASCAL VOC Parsing Pyramid urban landscape Pascal

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Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion 30

Simultaneous Facial Landmark Detection, Pose and Deformation...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Yue Gou, Chao Ji, Qiang Rensselaer Polytech Inst ECSE Dept Troy NY 12181 USA Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781538604571

Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. the existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, and the proposed model is robust to facial occlusion. Following a cascade procedure augmented with model-based head pose estimation, we iteratively update the facial landmark locations, facial occlusion, head pose and facial deformation until convergence. the experimental results on benchmark databases demonstrate the effectiveness of the proposed method for simultaneous facial landmark detection, head pose and facial deformation estimation, even if the images are under facial occlusion.

关键词： Head three-dimensional displays Strain Pose estimation Deformable models Two dimensional displays

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A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching 30

A Study of Lagrangean Decompositions and Dual Ascent Solvers...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Swoboda, Paul Rother, Carsten Abu Alhaija, Hassan Kainmueller, Dagmar Savchynskyy, Bogdan IST Austria Klosterneuburg Austria Tech Univ Dresden Dresden Germany MPI CBG Dresden Germany

ISBN: (纸本)9781538604571

We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore this direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives several theoretical insights and suggests a new state-of-the-art any-time solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each.

关键词： Message passing Labeling computer vision Schedules pattern matching Optimization Graph matching message passing images computer vision pattern matching ascent Solvers

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Multi-task Correlation Particle Filter for Robust Object Tracking 30

Multi-task Correlation Particle Filter for Robust Object Tra...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Tianzhu Xu, Changsheng Yang, Ming-Hsuan Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Calif Merced Merced CA USA

ISBN: (纸本)9781538604571

In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different features into account to learn correlation filters jointly. the proposed MCPF is designed to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has several advantages. First, it can shepherd the sampled particles toward the modes of the target state distribution via the MCF, thereby resulting in robust tracking performance. Second, it can effectively handle large-scale variation via a particle sampling strategy. third, it can effectively maintain multiple modes in the posterior density using fewer particles than conventional particle filters, thereby lowering the computational cost. Extensive experimental results on three benchmark datasets demonstrate that the proposed MCPF performs favorably against the state-of-the-art methods.

关键词： Correlation Target tracking Visualization Robustness Computational efficiency Benchmark testing

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AMVH: Asymmetric Multi-Valued Hashing 30

AMVH: Asymmetric Multi-Valued Hashing

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Da, Cheng Xu, Shibiao Ding, Kun Meng, Gaofeng Xiang, Shiming Pan, Chunhong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538604571

Most existing hashing methods resort to binary codes for similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose an asymmetric multi-valued hashing method supported by two different non-binary embeddings. (1) A real-valued embedding is used for representing the newly-coming query. (2) A multi-integer-embedding is employed for compressing the whole database, which is modeled by binary sparse representation with fixed sparsity. With these two non-binary embeddings, the similarities between data points can be preserved precisely. To perform meaningful asymmetric similarity computation for efficient semantic search, these embeddings are jointly learnt by preserving the label-based similarity. Technically, this results in a mixed integer programming problem, which is efficiently solved by alternative optimization. Extensive experiments on three multilabel datasets demonstrate that our approach not only outperforms the existing binary hashing methods in search accuracy, but also retains their query and storage efficiency.

关键词： hashing binary code hashing method

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