Modality-Guided Dynamic Graph Fusion and Temporal Diffusion …
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- DOI 10.24963/ijcai.2025/159
- OpenAlex ID W4414360127
- Type Conference-paper
- Language EN
- Published 01 Sep 2025
- Pages 1422–1430
- Source International Joint Conference on Artifi…
- FWCI 0.80
- Countries 1
- Institutions 1
- Added 10 Oct 2025
- Updated 12 Oct 2026
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Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
Conference-paper 2025 EN Bronze
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,…
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8 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Shenglan Li | China University of Mining and… (CN) | |
| 2 | Rui Yao | China University of Mining and… (CN) | |
| 3 | Yong Zheng Zhou | China University of Mining and… (CN) | |
| 4 | Hancheng Zhu | China University of Mining and… (CN) | |
| 5 | Kunyang Sun | China University of Mining and… (CN) | |
| 6 | Bing Liu | China University of Mining and… (CN) | |
| 7 | Zhiwen Shao | China University of Mining and… (CN) | |
| 8 | Jiaqi Zhao | China University of Mining and… (CN) |
Data: OpenAlex