AVLTrack: Dynamic Sparse Learning for Aerial Vision-Language Tracking

The introduction of natural language for vision-language (VL) tracking has been proven to improve performance. However, natural language remains under-explored in existing aerial trackers. Moreover, existing VL trackers ignore the misalignment of language with dynamic target states, which is prominent in complex UAV scenarios. In this work, we present AVLTrack, a flexible framework for aerial vision-language tracking. It consists of three key components, a dynamic sparse learning (DSL) module, an efficient Transformer backbone, and a multi-level language perception (MLP) strategy. First, DSL sparsely connects language and images via dynamic sparse attention, providing accurate multi-modal prompts. To adapt to…

• NN
  National Natural Science Foundation of China

  Awards: 62472109, 62466051, U23A20383, 62475288
• FF
  Foundation for Science and Technology

  Awards: 2025GXNSFAA069676, 2025GXNSFAA069417, 2024GXNSFGA010001