面向高效非对称无人机跟踪的双分支蒸馏 Transformer
Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking
面向视觉跟踪的教师引导双分支蒸馏方案（特征级+预测级+目标感知细粒度蒸馏）可直接迁移到任意轻量骨干的跟踪器或检测器上，用于在压缩骨干的同时保住目标表征与定位精度；推理期时序自适应模块也提供了一个低开销提升时序鲁棒性的可复用插件。
原文依据 a teacher-guided dual-branch distillation strategy that enhances the feature expressiveness of the lightweight student model
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论鉴于无人机跟踪的实时性需求，许多方法通过简化骨干网络来降低计算量，但这往往会削弱特征表达能力，并在复杂场景下导致性能下降。为缓解这一问题，我们提出 EATrack，一个高效且非对称的无人机跟踪框架，其核心是以教师引导的双分支蒸馏策略增强轻量学生模型的特征表达能力。具体而言，EATrack 从两个互补的知识迁移视角展开研究：一是空间聚焦的特征级蒸馏，通过引导学生学习强目标表征来补偿被削弱的表示；二是预测级蒸馏，通过学习教师准确定位目标的能力来增强空间定位。此外，为提升对外观变化的鲁棒性，我们引入细粒度目标感知蒸馏策略，有选择地将教师的目标建模能力迁移给学生。推理阶段还加入了时序自适应模块，以增强随时间变化的鲁棒性。在五个无人机基准上的实验表明，EATrack 在精度与速度之间取得了良好平衡，其中 EATrack-DeiT 在 GPU 上以 241.9 FPS 运行的同时，平均成功率较此前最优方法提升 1.2%。
英文原文摘要 1353 字符
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: a spatially focused feature-level distillation that compensates for weakened representations by guiding the student to learn strong target representations, and a prediction-level distillation that enhances spatial localization by learning the teacher's capability of accurate target localization. Furthermore, to enhance robustness against appearance variations, we introduce a fine-grained target-aware distillation strategy that selectively transfers the teacher's target modeling capacity to the student. A temporal adaptation module is incorporated at inference to enhance robustness over time. Experiments on five UAV benchmarks demonstrate that EATrack achieves a favorable balance between accuracy and speed, with EATrack-DeiT improving average success rate by 1.2% over the previous SOTA while running at 241.9 FPS on GPU.