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About this paper
Xiaojun Hou, Jiazheng Xing, Yijie Qian, Yaowei Guo, Shuo Xin, Junhao Chen, Kai Tang, Mengmeng Wang and 3 more
SDSTrack introduces a self-distillation symmetric adapter learning framework for multi-modal visual object tracking, achieving new state-of-the-art performance across RGB-D, RGB-T, and RGB-E benchmarks. This framework symmetrically integrates diverse sensor inputs and employs a complementary masked patch distillation strategy to enhance tracking robustness in challenging conditions by reducing reliance on any single modality.
Key insights
- A symmetric adapter-based approach can efficiently and effectively adapt pre-trained RGB trackers to multimodal data, overcoming modality dominance by treating all modalities equally.
- Reusing and adapting existing transformer blocks with specific attention masks allows for effective, parameter-efficient multimodal feature fusion.
- Self-distillation combined with complementary masking during training can enhance tracker robustness to sensor degradation and partial information by forcing the model to learn from incomplete data.
Summary by alphaXiv.
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