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LRDNN: Local-refining based deep neural network for person re-identification with attribute discerning
Zhou, Qinqin1; Zhong BN(钟必能)1,2; Lan, Xiangyuan3; Sun G(孙干)4,5; Zhang, Yulun6; Gou, Mengran6
Department机器人学研究室
Conference Name28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Conference DateAugust 10-16, 2019
Conference PlaceMacao, China
Author of SourceBaidu ; et al. ; Huawei ; International Joint Conferences on Artifical Intelligence (IJCAI) ; Sony ; Xiao-i
Source PublicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
PublisherInternational Joint Conferences on Artificial Intelligence
2019
Pages1041-1047
Indexed ByEI
EI Accession number20194607696708
Contribution Rank4
ISSN1045-0823
ISBN978-0-9992411-4-1
AbstractRecently, pose or attribute information has been widely used to solve person re-identification (re-ID) problem. However, the inaccurate output from pose or attribute modules will impair the final person re-ID performance. Since re-ID, pose estimation and attribute recognition are all based on the person appearance information, we propose a Local-refining based Deep Neural Network (LRDNN) to aggregate pose estimation and attribute recognition to improve the re-ID performance. To this end, we add a pose branch to extract the local spatial information and optimize the whole network on both person identity and attribute objectives. To diminish the negative affect from unstable pose estimation, a novel structure called channel parse block (CPB) is introduced to learn weights on different feature channels in pose branch. Then two branches are combined with compact bilinear pooling. Experimental results on Market1501 and DukeMTMC-reid datasets illustrate the effectiveness of the proposed method.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25875
Collection机器人学研究室
Affiliation1.Department of Computer Science and Technology, Huaqiao University, China
2.Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China
3.Department of Computer Science, Hong Kong Baptist University, Hong Kong
4.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
5.University of Chinese Academy of Sciences, China
6.Department of ECE, Northeastern University, United States
Recommended Citation
GB/T 7714
Zhou, Qinqin,Zhong BN,Lan, Xiangyuan,et al. LRDNN: Local-refining based deep neural network for person re-identification with attribute discerning[C]//Baidu, et al., Huawei, International Joint Conferences on Artifical Intelligence (IJCAI), Sony, Xiao-i:International Joint Conferences on Artificial Intelligence,2019:1041-1047.
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