AI RESEARCH
Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking
arXiv CS.CV
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ArXi:2605.28018v1 Announce Type: new 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.