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Deep Representation Learning With Part Loss for Person Re-Identification

文献类型：期刊论文

作者Yao, Hantao1; Zhang, Shiliang2; Hong, Richang3; Zhang, Yongdong4; Xu, Changsheng1,5; Tian, Qi6
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2019-06-01
卷号28期号:6页码:2860-2871
ISSN号1057-7149
关键词Person re-identification representation learning part lass networks convolutional neural networks
DOI10.1109/TIP.2019.2891888
通讯作者Yao, Hantao(hantao.yao@nlpr.ia.ac.cn)
英文摘要Learning discriminative representations for unseen person images is critical for person re-identification (ReID). Most of the current approaches learn deep representations in classification tasks, which essentially minimize the empirical classification risk on the training set. As shown in our experiments, such representations easily get over-fitted on a discriminative human body part on the training set. To gain the discriminative power on unseen person images, we propose a deep representation learning procedure named part loss network, to minimize both the empirical classification risk on training person images and the representation learning risk on unseen person images. The representation learning risk is evaluated by the proposed part loss, which automatically detects human body parts and computes the person classification loss on each part separately. Compared with traditional global classification loss, simultaneously considering part loss enforces the deep network to learn representations for different body parts and gain the discriminative power on unseen persons. Experimental results on three person ReID datasets, i.e., Market1501, CUHK03, and VIPeR, show that our representation outperforms existing deep representations.
资助项目National Postdoctoral Programme for Innovative Talents ; National Nature Science Foundation of China[61525206] ; National Nature Science Foundation of China[61532009] ; National Nature Science Foundation of China[61721004] ; National Nature Science Foundation of China[U1705262] ; National Nature Science Foundation of China[61572050] ; National Nature Science Foundation of China[91538111] ; National Nature Science Foundation of China[61620106009] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000462386000018
资助机构National Postdoctoral Programme for Innovative Talents ; National Nature Science Foundation of China ; Key Research Program of Frontier Sciences, CAS
源URL[http://ir.ia.ac.cn/handle/173211/23474]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Yao, Hantao
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China
3.Univ Technol, Dept Comp Sci & Technol, Hefei 230009, Anhui, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Dept Artificial Intelligence, Beijing 100049, Peoples R China
6.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
推荐引用方式
GB/T 7714		 Yao, Hantao,Zhang, Shiliang,Hong, Richang,et al. Deep Representation Learning With Part Loss for Person Re-Identification[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(6):2860-2871.
APA Yao, Hantao,Zhang, Shiliang,Hong, Richang,Zhang, Yongdong,Xu, Changsheng,&Tian, Qi.(2019).Deep Representation Learning With Part Loss for Person Re-Identification.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(6),2860-2871.
MLA Yao, Hantao,et al."Deep Representation Learning With Part Loss for Person Re-Identification".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.6(2019):2860-2871.

入库方式： OAI收割

来源：自动化研究所

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