SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking

• URL: http://arxiv.org/abs/2403.16002v2
• Date: Thu, 28 Mar 2024 03:22:52 GMT
• Title: SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
• Authors: Xiaojun Hou, Jiazheng Xing, Yijie Qian, Yaowei Guo, Shuo Xin, Junhao Chen, Kai Tang, Mengmeng Wang, Zhengkai Jiang, Liang Liu, Yong Liu,
• Abstract summary: Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. We propose a novel symmetric multimodal tracking framework called SDSTrack.
• Score: 19.50096632818305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers, which was inefficient and lacked generalized representation due to the scarcity of multimodal data. Therefore, recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. However, the modality gap limits pre-trained knowledge recall, and the dominance of the RGB modality persists, preventing the full utilization of information from other modalities. To address these issues, we propose a novel symmetric multimodal tracking framework called SDSTrack. We introduce lightweight adaptation for efficient fine-tuning, which directly transfers the feature extraction ability from RGB to other domains with a small number of trainable parameters and integrates multimodal features in a balanced, symmetric manner. Furthermore, we design a complementary masked patch distillation strategy to enhance the robustness of trackers in complex environments, such as extreme weather, poor imaging, and sensor failure. Extensive experiments demonstrate that SDSTrack outperforms state-of-the-art methods in various multimodal tracking scenarios, including RGB+Depth, RGB+Thermal, and RGB+Event tracking, and exhibits impressive results in extreme conditions. Our source code is available at https://github.com/hoqolo/SDSTrack.

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