轻量级无人机追踪模型通过双分支知识蒸馏提升精度与速度
Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking
- 采用教师指导的双分支蒸馏策略，强化轻量模型特征表达
- 在5个无人机数据集上实现高精度与实时追踪的平衡
- 适合对实时性要求高的无人机视觉追踪场景
为满足无人机追踪的实时性需求，现有方法常简化主干网络以降低计算量，但会削弱特征表示能力，导致复杂场景下性能下降。为此，我们提出EATrack——一种高效且非对称的无人机追踪框架，核心是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从两个互补角度进行知识迁移：空间聚焦的特征级蒸馏，使学生模型学习到强目标表征；预测级蒸馏，提升学生对目标空间定位的准确性。此外，为增强外观变化下的鲁棒性，引入细粒度目标感知蒸馏策略，选择性传递教师模型的目标建模能力。推理阶段集成时序自适应模块，进一步提升长期追踪稳定性。在五个无人机基准数据集上的实验表明，EATrack在准确率与速度间取得良好平衡。
原文摘要 · Abstract (English)
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 distillation that enhances spatial localization by learning the teacher's capability for 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. Code: https://github.com/GXNU-ZhongLab/EATrack
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