{"type":"ScholarlyArticle","title":"LRDNN: Local-refining based Deep Neural Network for Person Re-Identification with Attribute Discerning","authors":[{"name":"Qinqin Zhou","slug":"qinqin-zhou","given":"Qinqin","family":"Zhou","affiliation":"Department of Computer Science and Technology , Huaqiao University"},{"name":"Bineng Zhong","slug":"bineng-zhong","given":"Bineng","family":"Zhong","affiliation":"Department of Computer Science and Technology , Huaqiao University; Provincial Key Laboratory for Computer Information Processing Technology , Soochow University"},{"name":"Xiangyuan Lan","slug":"xiangyuan-lan","given":"Xiangyuan","family":"Lan","affiliation":"Department of Computer Science , Hong Kong Baptist University"},{"name":"Gan Sun","slug":"gan-sun","given":"Gan","family":"Sun","affiliation":"State Key Laboratory of Robotics , Shenyang Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Yulun Zhang","slug":"yulun-zhang","given":"Yulun","family":"Zhang","affiliation":"Department of ECE , Northeastern University"},{"name":"Mengran Gou","slug":"mengran-gou","given":"Mengran","family":"Gou","affiliation":"Department of ECE , Northeastern University"}],"abstract":"Recently, 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.","doi":"10.24963/ijcai.2019/146","url":"https://ijcai.org/proceedings/2019/146","pdf":"https://ijcai.org/proceedings/2019/0146.pdf","markdown":"https://ijcai.org/proceedings/2019/146.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/146","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Computer Vision","track":"Main track","number":146,"pages":{"first":1041,"last":1047},"keywords":["Computer Vision: Computer Vision","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2018] Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 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