{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:38:03Z","timestamp":1773801483118,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Video-based visible-infrared person re-identification (VVI-ReID) aims to match pedestrian sequences across modalities for all-day surveillance. While supervised methods have shown progress, their dependence on large-scale cross-modal annotations limits scalability.\nWe investigate the task of unsupervised domain adaptation for VVI-ReID (UDA-VVI-ReID), where a model trained on a labeled source domain is adapted to an unlabeled target domain. Directly extending existing image-based unsupervised VI-ReID methods to video scenarios by simply averaging frame-level features is suboptimal, as this naive strategy neglects the rich temporal dynamics in video data and leads to unreliable pseudo-labels due to occlusion-induced noise.\nTo overcome these limitations, we propose a Dynamic-Static Collaboration (DSC) framework that explicitly leverages the complementary strengths of motion and appearance cues. The Dynamic-Static Label Unification (DSLU) module refines pseudo-labels by validating the consistency between static and dynamic predictions. Based on these labels, the Dynamic-Static Joint Learning (DSJL) module performs neighbor-aware contrastive learning in both feature spaces, promoting robust representation learning under cross-modal and temporal variations.\nExperiments on HITSZ-VCM and BUPTCampus show that DSC sets a strong baseline for this new task, enabling robust cross-modal video ReID without target labels.<\/jats:p>","DOI":"10.1609\/aaai.v40i8.37518","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:28:23Z","timestamp":1773790103000},"page":"5955-5963","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic-Static Collaboration for Unsupervised Domain Adaptive Video-Based Visible-Infrared Person Re-Identification"],"prefix":"10.1609","volume":"40","author":[{"given":"Jiaxu","family":"Leng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengjie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinbo","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37518\/41480","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37518\/41480","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:28:24Z","timestamp":1773790104000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i8.37518","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}