Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking

Shenglan Li; Rui Yao; Yong Zhou; Hancheng Zhu; Kunyang Sun; Bing Liu; Zhiwen Shao; Jiaqi Zhao

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arXiv · 2505.03507

Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking

Shenglan Li·Rui Yao·Yong Zhou·Hancheng Zhu·Kunyang Sun·Bing Liu·Zhiwen Shao·Jiaqi Zhao

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Abstract

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's 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. The source code is available at https://github.com/LiShenglana/GDSTrack.

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BibTeX RIS

Shenglan Li, Rui Yao, Yong Zhou, Hancheng Zhu, Kunyang Sun, Bing Liu, Zhiwen Shao, Jiaqi Zhao. 2025-05-06. Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking. https://arxiv.org/abs/2505.03507

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