{"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/exploiting-robust-unsupervised-video-person","title":"Exploiting Robust Unsupervised Video Person Re-identification","arxiv_id":"2111.05170","date":"2021-11-09","proceeding":null,"authors":["Xianghao Zang","Ge Li","Wei Gao","Xiujun Shu"],"abstract":"Unsupervised video person re-identification (reID) methods usually depend on global-level features. And many supervised reID methods employed local-level features and achieved significant performance improvements. However, applying local-level features to unsupervised methods may introduce an unstable performance. To improve the performance stability for unsupervised video reID, this paper introduces a general scheme fusing part models and unsupervised learning. In this scheme, the global-level feature is divided into equal local-level feature. A local-aware module is employed to explore the poentials of local-level feature for unsupervised learning. A global-aware module is proposed to overcome the disadvantages of local-level features. Features from these two modules are fused to form a robust feature representation for each input image. This feature representation has the advantages of local-level feature without suffering from its disadvantages. Comprehensive experiments are conducted on three benchmarks, including PRID2011, iLIDS-VID, and DukeMTMC-VideoReID, and the results demonstrate that the proposed approach achieves state-of-the-art performance. Extensive ablation studies demonstrate the effectiveness and robustness of proposed scheme, local-aware module and global-aware module. The code and generated features are available at https://github.com/deropty/uPMnet.","url_abs":"https://arxiv.org/abs/2111.05170v3","url_pdf":"https://arxiv.org/pdf/2111.05170v3.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":"exploiting-robust-unsupervised-video-person","repo_url":"https://github.com/deropty/uPMnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"unsupervised-person-re-identification","task_name":"Unsupervised Person Re-Identification"},{"task_slug":"video-based-person-re-identification","task_name":"Video-Based Person Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-prid2011","task":"Person Re-Identification","dataset":"PRID2011","model":"uPMnet","rank_in_archive_order":5,"of":13,"metrics":{"Rank-1":"92.0","Rank-20":"100.0","Rank-5":"97.7"},"uses_additional_data":false},{"leaderboard":"/sota/person-re-identification-on-ilids-vid","task":"Person Re-Identification","dataset":"iLIDS-VID","model":"uPMnet","rank_in_archive_order":8,"of":10,"metrics":{"Rank-1":"63.1","Rank-20":"92.5","Rank-5":"81.9"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-person-re-identification-on-11","task":"Unsupervised Person Re-Identification","dataset":"DukeMTMC-VideoReID","model":"uPMnet","rank_in_archive_order":2,"of":2,"metrics":{"Rank-1":"83.6","Rank-20":"97.2","Rank-5":"93.1","mAP":"76.9"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-person-re-identification-on-9","task":"Unsupervised Person Re-Identification","dataset":"PRID2011","model":"uPMnet","rank_in_archive_order":1,"of":1,"metrics":{" Rank-1":"92.00","Rank-20":"100.0","Rank-5":"97.7"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-person-re-identification-on-10","task":"Unsupervised Person Re-Identification","dataset":"iLIDS-VID","model":"uPMnet","rank_in_archive_order":1,"of":1,"metrics":{" Rank-1":"63.1","Rank-20":"92.5","Rank-5":"81.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}