{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:40:37Z","timestamp":1773801637715,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Most existing RGB-Event trackers rely on strictly aligned datasets, overlooking the asynchronous spatio-temporal resolutions common in real-world scenarios. \nThis methodological limitation impedes effective RGB-Event feature alignment and ultimately degrades tracking performance.\nTo overcome this limitation, we propose AlignTrack, a novel tracking framework built upon a Top-Down Alignment (TDA) strategy inspired by the human visual system. \nOur TDA framework follows an encode-decode-align paradigm: it first encodes multimodal features to generate target-related priors, which are then progressively decoded to guide a subsequent feature alignment pass. \nWithin this framework, we introduce two key innovations: (1) a Cross-Prior Attention (CPA) module that effectively generates and integrates cross-modal priors, and (2) a Cross-Modal Semantic Alignment (CSA) loss that maximizes mutual information to enforce semantic consistency between modalities. \nExtensive experiments show that AlignTrack achieves state-of-the-art performance on four challenging RGB-Event tracking benchmarks, demonstrating its robustness in both aligned and unaligned scenarios. \nAblation studies further validate the significant contribution of each proposed component.<\/jats:p>","DOI":"10.1609\/aaai.v40i11.37874","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:48:51Z","timestamp":1773791331000},"page":"9171-9179","source":"Crossref","is-referenced-by-count":0,"title":["AlignTrack: Top-Down Spatiotemporal Resolution Alignment for RGB-Event Visual Tracking"],"prefix":"10.1609","volume":"40","author":[{"given":"Chuanyu","family":"Sun","sequence":"first","affiliation":[]},{"given":"Jiqing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuanchen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yutong","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[]}],"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\/37874\/41836","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37874\/41836","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:48:51Z","timestamp":1773791331000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37874"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i11.37874","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]]}}}