Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking
Given the real-time demands of UAV tracking, many methods simplify the backbone to reduce computation, but this often weakens feature representation and degrades performance in complex scenarios. To alleviate this issue, we propose EATrack, an efficient and asymmetric UAV tracking framework centered around a teacher-guided dual-branch distillation strategy that enhances the feature expressiveness of the lightweight student model. Specifically, EATrack investigates two complementary perspectives of knowledge transfer: spatially focused feature-level distillation that compensates for weakened representations by guiding the student to learn strong target representations, and prediction-level...
| Authors | Hongtao Yang, Bineng Zhong, Qihua Liang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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