{"id":1179086,"date":"2025-02-28T00:00:00","date_gmt":"2025-02-28T08:00:00","guid":{"rendered":"https:\/\/research.codeghost.online\/en-us\/research\/?post_type=msr-research-item&#038;p=1179086"},"modified":"2026-07-20T06:39:38","modified_gmt":"2026-07-20T13:39:38","slug":"multi-clues-adaptive-learning-for-cloth-changing-person-re-identification","status":"publish","type":"msr-research-item","link":"https:\/\/research.codeghost.online\/en-us\/research\/publication\/multi-clues-adaptive-learning-for-cloth-changing-person-re-identification\/","title":{"rendered":"Multi-clues Adaptive Learning for Cloth-Changing Person Re-Identification"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Solving long-term Cloth-Changing Person Re-identification (CC-ReID) requires extracting features insensitive to clothing such as face, silhouette, gait and pose estimation. Most current work focuses on modeling from a single feature, but we observe that CC-ReID problems in open environments are often difficult to solve solely based on a single feature, for instance, sometimes, contour features may be advantageous for recognition, while at other times gait features may be more valuable. In our paper, we suggest a novel multi-clues guided Adaptive Learning Transformer (ALT) which can adaptively select the most readily identifiable features based on different scenarios. The method comprises two parts: a Multi-clues Guiding Module (MGM) and a Feature Selection Module (FSM). We utilize clothes-irrelevant features from multi-modality information as clues, integrating multiple features to extract robust representations invariant to clothing changes for CC-ReID through cross-attention and Mixture of Experts (MoEs). We utilized contour sketch and gait as clues, conducting experiments on the CC-ReID dataset. The experimental results show that our recommended approach prevails over all other SOTA methods, particularly showing significant improvement compared to using contour sketch and gait alone.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Solving long-term Cloth-Changing Person Re-identification (CC-ReID) requires extracting features insensitive to clothing such as face, silhouette, gait and pose estimation. Most current work focuses on modeling from a single feature, but we observe that CC-ReID problems in open environments are often difficult to solve solely based on a single feature, for instance, sometimes, contour features [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Xiang Zhou","user_id":0},{"type":"text","value":"Junzhu Liu","user_id":0},{"type":"user_nicename","value":"Xinyang Jiang","user_id":"41802"},{"type":"text","value":"Pengyu Li","user_id":0},{"type":"text","value":"Cairong Zhao","user_id":0}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"International journal of pattern recognition and artificial intelligence","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Int. 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