ATA: A Benchmark for Vision–Language Tracking in Air-to-Air Counter-UAV of Tiny Drones

In air-to-air counter-UAV scenarios, vision–language tracking for tiny drones still lacks a dedicated benchmark. Unlike traditional UAV tracking or ground-based Anti-UAV settings, air-to-air counter-UAV tracking involves simultaneous motion of both the tracking platform and the target platform. In addition, the target typically appears as a tiny object and is subject to rapid viewpoint changes, fast background transitions, and interference from similar drones, making it difficult to systematically assess the capability boundaries of existing methods. To address this gap, we present the ATA dataset. To the best of our knowledge, ATA is the first vision–language tracking dataset specifically designed for real air-to-air tiny-object UAV countermeasure scenarios. ATA contains 50 real-flight video sequences with 38,094 frames in total, and provides frame-wise bounding box annotations together with video-level English language descriptions. It supports two unified task settings, namely BBox-only and Language-assisted tracking. The dataset covers diverse real-world low-altitude scenarios with complex backgrounds. Notably, the average target area accounts for only 0.03% of the full image, exhibiting pronounced tiny-object characteristics. ATA also captures several key challenges in this setting, including dual-dynamic disturbances, complex background changes, and multi-drone interference. Based on ATA, we establish a benchmark covering both vision-only and vision–language tracking methods, and conduct a systematic evaluation of eight representative recent trackers. Experimental results show that current mainstream methods still perform unsatisfactorily in this scenario, with evident limitations in tiny-object representation, cross-frame association, robustness to complex backgrounds, and interference suppression. Furthermore, we validate a lightweight temporal enhancement module, AFTE, and show that explicitly leveraging adjacent-frame information consistently improves the performance of multiple baseline models. Overall, ATA provides a unified benchmark for vision–language tracking in air-to-air counter-UAV scenarios of tiny drones and highlights temporal modeling as a promising direction for improving tracking performance in this challenging setting.