CASIA OpenIR
Deep Representation Learning With Part Loss for Person Re-Identification
Yao, Hantao1; Zhang, Shiliang2; Hong, Richang3; Zhang, Yongdong4; Xu, Changsheng1,5; Tian, Qi6
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-06-01
Volume28Issue:6Pages:2860-2871
Corresponding AuthorYao, Hantao(hantao.yao@nlpr.ia.ac.cn)
AbstractLearning 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.
KeywordPerson re-identification representation learning part lass networks convolutional neural networks
DOI10.1109/TIP.2019.2891888
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Postdoctoral Programme for Innovative Talents ; National Nature Science Foundation of China ; Key Research Program of Frontier Sciences, CAS
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000462386000018
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23475
Collection中国科学院自动化研究所
Corresponding AuthorYao, Hantao
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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