{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:30:04Z","timestamp":1771705804891,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>In real applications, person re-identification (ReID) expects to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets cannot meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first large-scale dataset, AT-USTC, which contains 135k images of individuals wearing multiple clothes captured by RGB and IR cameras. Our data collection spans over an entire year and 270 volunteers were photographed on average 29.1 times across different dates or scenes, 4-15 times more than current datasets, providing conditions for follow-up investigations in AT-ReID. Further, to tackle the new challenge of multi-scenario retrieval, we propose a unified model named Uni-AT, which comprises a multi-scenario ReID (MS-ReID) framework for scenario-specific features learning, a Mixture-of-Attribute-Experts (MoAE) module to alleviate inter-scenario interference, and a Hierarchical Dynamic Weighting (HDW) strategy to ensure balanced training across all scenarios. Extensive experiments show that our model leads to satisfactory results and exhibits excellent generalization to all scenarios.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/164","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"1467-1475","source":"Crossref","is-referenced-by-count":1,"title":["Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification"],"prefix":"10.24963","author":[{"given":"Xulin","family":"Li","sequence":"first","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China"},{"name":"Anhui Province Key Laboratory of Digital Security"}]},{"given":"Yan","family":"Lu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China"},{"name":"Anhui Province Key Laboratory of Digital Security"}]},{"given":"Jiaze","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China"},{"name":"Anhui Province Key Laboratory of Digital Security"}]},{"given":"Qinhong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China"},{"name":"Anhui Province Key Laboratory of Digital Security"}]},{"given":"Tao","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China"},{"name":"Anhui Province Key Laboratory of Digital Security"}]},{"given":"Qi","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China"},{"name":"Anhui Province Key Laboratory of Digital Security"}]},{"given":"Mang","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, China"}]},{"given":"Nenghai","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China"},{"name":"Anhui Province Key Laboratory of Digital Security"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:11Z","timestamp":1758627191000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/164"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/164","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}