{"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/long-term-person-re-identification-with","title":"Long-Term Person Re-identification with Dramatic Appearance Change: Algorithm and Benchmark","arxiv_id":null,"date":"2022-10-01","proceeding":"MM '22: Proceedings of the 30th ACM International Conference on Multimedia 2022 10","authors":["Mengmeng Liu","Zhi Ma","Tao Li","Yanfeng Jiang","Kai Wang"],"abstract":"For person re-identification (Re-ID) task, most of previous studies assumed that the pedestrians do not change their appearances. The works on cross-appearance Re-ID, including datasets and algorithms, are still few. Therefore, this paper contributes a cross-season appearance change Re-ID dataset, namely NKUP+, including more than 300 IDs from surveillance videos over 10 months, to support the studies of the cross-appearance Re-ID. In addition, we propose a network named M2Net, which integrates multi-modality features from the RGB images, contour images and human parsing images. By ignoring irrelevant misleading information for cross-appearance retrieval in RGB images, M2Net can learn features that are robust to appearance changes. Meanwhile, we propose a sampling strategy called RAS to contain a variety of appearances in one batch. And appearance loss and multi-appearance loss are designed to guide the network to learn both same-appearance and cross-appearance features. Finally, we evaluated our method on NKUP+/PRCC/DeepChange datasets, and the results showed that, compared with the baseline, our method renders significant improvement, leading to the state-of-the-art performance over other methods. Our dataset is available at https://github.com/nkicsl/NKUP-dataset.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3503161.3548327","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3503161.3548327","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":"long-term-person-re-identification-with","repo_url":"https://github.com/nkicsl/nkup-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"long-term-person-re-identification-with","repo_url":"https://github.com/JoegameZhou/mindspore-m2net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"long-term-person-re-identification-with","repo_url":"https://github.com/lyqcom/m2net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"human-parsing","task_name":"Human Parsing"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}