Learning Motion Blur Robust Vision Transformers with Dynamic Early Exit for Real-Time UAV Tracking
<|reference_start|>Learning Motion Blur Robust Vision Transformers with Dynamic Early Exit for Real-Time UAV Tracking: Recently, the surge in the adoption of single-stream architectures utilizing pre-trained ViT backbones represents a promising advancement in the field of generic visual tracking. By integrating feature extraction and fusion into a cohesive framework, these architectures offer improved performance, efficiency, and robustness. However, there has been limited exploration into optimizing these frameworks for UAV tracking. In this paper, we boost the efficiency of this framework by tailoring it into an adaptive computation framework that dynamically exits Transformer blocks for real-time UAV tracking. The motivation behind this is that tracking tasks with fewer challenges can be adequately addressed using low-level feature representations. Simpler tasks can often be handled with less demanding, lower-level features. This approach allows the model use computational resources more efficiently by focusing on complex tasks and conserving resources for easier ones. Another significant enhancement introduced in this paper is the improved effectiveness of ViTs in handling motion blur, a common issue in UAV tracking caused by the fast movements of either the UAV, the tracked objects, or both. This is achieved by acquiring motion blur robust representations through enforcing invariance in the feature representation of the target with respect to simulated motion blur. The proposed approach is dubbed BDTrack. Extensive experiments conducted on five tracking benchmarks validate the effectiveness and versatility of our approach, establishing it as a cutting-edge solution in real-time UAV tracking. Code is released at: https://github.com/wuyou3474/BDTrack.<|reference_end|>
@article{wu2024learning,
    title={Learning Motion Blur Robust Vision Transformers with Dynamic Early Exit
  for Real-Time UAV Tracking},
    author={You Wu, Xucheng Wang, Dan Zeng, Hengzhou Ye, Xiaolan Xie, Qijun Zhao,
  and Shuiwang Li},
    journal={arXiv preprint arXiv:2407.05383},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05383},
    primaryClass={cs.CV}
}