{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:00Z","timestamp":1758672900369,"version":"3.44.0"},"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>To reduce the reliance on large-scale annotations, self-supervised RGB-T tracking approaches have garnered significant attention. However, the omission of the object region by erroneous pseudo-label or the introduction of background noise affects the efficiency of modality fusion, while pseudo-label noise triggered by similar object noise can further affect the tracking performance. In this paper, we propose GDSTrack, a novel approach that introduces dynamic graph fusion and temporal diffusion to address the above challenges in self-supervised RGB-T tracking. GDSTrack dynamically fuses the modalities of neighboring frames, treats them as distractor noise, and leverages the denoising capability of a generative model. Specifically, by constructing an adjacency matrix via an Adjacency Matrix Generator (AMG), the proposed Modality-guided Dynamic Graph Fusion (MDGF) module uses a dynamic adjacency matrix to guide graph attention, focusing on and fusing the object\u2019s coherent regions. Temporal Graph-Informed Diffusion (TGID) models MDGF features from neighboring frames as interference, and thus improving robustness against similar-object noise. Extensive experiments conducted on four public RGB-T tracking datasets demonstrate that GDSTrack outperforms the existing state-of-the-art methods. \n\nThe source code is available at https:\/\/github.com\/LiShenglana\/GDSTrack.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/159","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"1422-1430","source":"Crossref","is-referenced-by-count":0,"title":["Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking"],"prefix":"10.24963","author":[{"given":"Shenglan","family":"Li","sequence":"first","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Yao","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hancheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunyang","family":"Sun","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Shao","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","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":"2025-09-23T11:33:10Z","timestamp":1758627190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/159"}},"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\/159","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}