{"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/hard-samples-rectification-for-unsupervised","title":"Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification","arxiv_id":"2106.07204","date":"2021-06-14","proceeding":null,"authors":["Chih-Ting Liu","Man-Yu Lee","Tsai-Shien Chen","Shao-Yi Chien"],"abstract":"Person re-identification (re-ID) has received great success with the supervised learning methods. However, the task of unsupervised cross-domain re-ID is still challenging. In this paper, we propose a Hard Samples Rectification (HSR) learning scheme which resolves the weakness of original clustering-based methods being vulnerable to the hard positive and negative samples in the target unlabelled dataset. Our HSR contains two parts, an inter-camera mining method that helps recognize a person under different views (hard positive) and a part-based homogeneity technique that makes the model discriminate different persons but with similar appearance (hard negative). By rectifying those two hard cases, the re-ID model can learn effectively and achieve promising results on two large-scale benchmarks.","url_abs":"https://arxiv.org/abs/2106.07204v1","url_pdf":"https://arxiv.org/pdf/2106.07204v1.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":[],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"},{"task_slug":"unsupervised-person-re-identification","task_name":"Unsupervised Person Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-person-re-identification-on-1","task":"Unsupervised Person Re-Identification","dataset":"DukeMTMC-reID->Market-1501","model":"HSR (Ours)","rank_in_archive_order":3,"of":8,"metrics":{"Rank-1":"85.3","mAP":"65.2"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-person-re-identification-on","task":"Unsupervised Person Re-Identification","dataset":"Market-1501->DukeMTMC-reID","model":"HSR (Ours)","rank_in_archive_order":4,"of":7,"metrics":{"Rank-1":"76.1","mAP":"58.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}