{"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/beyond-part-models-person-retrieval-with","title":"Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)","arxiv_id":"1711.09349","date":"2017-11-26","proceeding":"ECCV 2018 9","authors":["Yifan Sun","Liang Zheng","Yi Yang","Qi Tian","Shengjin Wang"],"abstract":"Employing part-level features for pedestrian image description offers\nfine-grained information and has been verified as beneficial for person\nretrieval in very recent literature. A prerequisite of part discovery is that\neach part should be well located. Instead of using external cues, e.g., pose\nestimation, to directly locate parts, this paper lays emphasis on the content\nconsistency within each part.\n  Specifically, we target at learning discriminative part-informed features for\nperson retrieval and make two contributions. (i) A network named Part-based\nConvolutional Baseline (PCB). Given an image input, it outputs a convolutional\ndescriptor consisting of several part-level features. With a uniform partition\nstrategy, PCB achieves competitive results with the state-of-the-art methods,\nproving itself as a strong convolutional baseline for person retrieval.\n  (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs\noutliers in each part, which are in fact more similar to other parts. RPP\nre-assigns these outliers to the parts they are closest to, resulting in\nrefined parts with enhanced within-part consistency. Experiment confirms that\nRPP allows PCB to gain another round of performance boost. For instance, on the\nMarket-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1\naccuracy, surpassing the state of the art by a large margin.","url_abs":"http://arxiv.org/abs/1711.09349v3","url_pdf":"http://arxiv.org/pdf/1711.09349v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/AndlollipopFU/PCB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/Calylyli/PCB_RPP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/Demonhesusheng/Reid","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/GuHongyang/Person-ReID-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/HoganZhang/Person_reID_baseline_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/NIRVANALAN/reid_baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/Proxim123/person-reID-No1-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/SurajDonthi/Clean-ST-ReID-Multi-Target-Multi-Camera-Tracking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/SurajDonthi/Multi-Camera-Person-Re-Identification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/SurajDonthi/Multi-Target-Multi-Camera-Tracking-ST-ReID","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/SurajDonthi/Multi-Target-Multi-Camera-Tracking-ST-ReID-Clean-Code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/huanghoujing/beyond-part-models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/huanghoujing/person-reid-triplet-loss-baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/ivychill/reid","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/jiangsikai/Person_reID_baseline_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/lsh110600/person_re_id","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/syfafterzy/PCB_RPP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/syfafterzy/pcb_rpp_for_reid","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/taroogura/Person_reID_baseline_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/wxb589/Person_reID_baseline_pytorch-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"beyond-part-models-person-retrieval-with","repo_url":"https://github.com/xuxu116/pytorch-reid-lite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"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