{"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-diverse-features-with-part-level","title":"Learning Diverse Features with Part-Level Resolution for Person Re-Identification","arxiv_id":"2001.07442","date":"2020-01-21","proceeding":null,"authors":["Ben Xie","Xiaofu Wu","Suofei Zhang","Shiliang Zhao","Ming Li"],"abstract":"Learning diverse features is key to the success of person re-identification. Various part-based methods have been extensively proposed for learning local representations, which, however, are still inferior to the best-performing methods for person re-identification. This paper proposes to construct a strong lightweight network architecture, termed PLR-OSNet, based on the idea of Part-Level feature Resolution over the Omni-Scale Network (OSNet) for achieving feature diversity. The proposed PLR-OSNet has two branches, one branch for global feature representation and the other branch for local feature representation. The local branch employs a uniform partition strategy for part-level feature resolution but produces only a single identity-prediction loss, which is in sharp contrast to the existing part-based methods. Empirical evidence demonstrates that the proposed PLR-OSNet achieves state-of-the-art performance on popular person Re-ID datasets, including Market1501, DukeMTMC-reID and CUHK03, despite its small model size.","url_abs":"https://arxiv.org/abs/2001.07442v1","url_pdf":"https://arxiv.org/pdf/2001.07442v1.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":"learning-diverse-features-with-part-level","repo_url":"https://github.com/AI-NERC-NUPT/PLR-OSNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-cuhk03-detected","task":"Person Re-Identification","dataset":"CUHK03 detected","model":"PLR-OSNet","rank_in_archive_order":6,"of":19,"metrics":{"MAP":"77.2","Rank-1":"80.4"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-cuhk03-labeled","task":"Person Re-Identification","dataset":"CUHK03 labeled","model":"PLR-OSNet","rank_in_archive_order":8,"of":21,"metrics":{"MAP":"80.5","Rank-1":"84.6"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-cuhk03-c","task":"Person Re-Identification","dataset":"CUHK03-C","model":"MGN","rank_in_archive_order":7,"of":8,"metrics":{" Rank-1":"5.44"," mAP":"4.20"," mINP":"0.46"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-dukemtmc-reid","task":"Person Re-Identification","dataset":"DukeMTMC-reID","model":"PLR-OSNet","rank_in_archive_order":39,"of":94,"metrics":{"Rank-1":"91.6","mAP":"81.2"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-market-1501","task":"Person Re-Identification","dataset":"Market-1501","model":"PLR-OSNet","rank_in_archive_order":49,"of":135,"metrics":{"Rank-1":"95.6","mAP":"88.9"},"uses_additional_data":true},{"leaderboard":"/sota/person-re-identification-on-market-1501-c","task":"Person Re-Identification","dataset":"Market-1501-C","model":"PLR-OS","rank_in_archive_order":3,"of":22,"metrics":{" Rank-1":"37.56"," mAP":"14.23"," mINP":"0.48"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2001.07442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.07442"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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