{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-discriminative-features-with","title":"Learning Discriminative Features with Multiple Granularities for Person Re-Identification","arxiv_id":"1804.01438","date":"2018-04-04","proceeding":null,"authors":["Guanshuo Wang","Yufeng Yuan","Xiong Chen","Jiwei Li","Xi Zhou"],"abstract":"The combination of global and partial features has been an essential solution\nto improve discriminative performances in person re-identification (Re-ID)\ntasks. Previous part-based methods mainly focus on locating regions with\nspecific pre-defined semantics to learn local representations, which increases\nlearning difficulty but not efficient or robust to scenarios with large\nvariances. In this paper, we propose an end-to-end feature learning strategy\nintegrating discriminative information with various granularities. We carefully\ndesign the Multiple Granularity Network (MGN), a multi-branch deep network\narchitecture consisting of one branch for global feature representations and\ntwo branches for local feature representations. Instead of learning on semantic\nregions, we uniformly partition the images into several stripes, and vary the\nnumber of parts in different local branches to obtain local feature\nrepresentations with multiple granularities. 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For example, on Market-1501 dataset in single query mode, we\nachieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.","url_abs":"http://arxiv.org/abs/1804.01438v3","url_pdf":"http://arxiv.org/pdf/1804.01438v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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