A2Seek: Towards Reasoning-Centric Benchmark for Aerial Anomaly Understanding

Mengjingcheng Mo, Xinyang Tong, Mingpi Tan, Jiaxu Leng, JianKang Zheng, Yiran Liu, Haosheng Chen, Ji Gan, Weisheng Li, Xinbo Gao

Keywords: Video Anomaly Detection, Scene Understanding, Multimodal Reasoning, Aerial Anomaly Understanding, Unmanned Aerial Vehicles

TL;DR: We introduce A2Seek, a reasoning-centric benchmark for aerial anomaly understanding, and A2Seek-R1, a method designed to seek and interpret anomalies.

Abstract:

While unmanned aerial vehicles (UAVs) offer wide-area, high-altitude coverage for anomaly detection, they face challenges such as dynamic viewpoints, scale variations, and complex scenes. Existing datasets and methods, mainly designed for fixed ground-level views, struggle to adapt to these conditions, leading to significant performance drops in drone-view scenarios.
To bridge this gap, we introduce A2Seek (Aerial Anomaly Seek), a large-scale, reasoning-centric benchmark dataset for aerial anomaly understanding. This dataset covers various scenarios and environmental conditions, providing high-resolution real-world aerial videos with detailed annotations, including anomaly categories, frame-level timestamps, region-level bounding boxes, and natural language explanations for causal reasoning.
Building on this dataset, we propose A2Seek-R1, a novel reasoning framework that generalizes R1-style strategies to aerial anomaly understanding, enabling a deeper understanding of “Where” anomalies occur and “Why” they happen in aerial frames.
To this end, A2Seek-R1 first employs a graph-of-thought (GoT)-guided supervised fine-tuning approach to activate the model's latent reasoning capabilities on A2Seek. Then, we introduce Aerial Group Relative Policy Optimization (A-GRPO) to design rule-based reward functions tailored to aerial scenarios. Furthermore, we propose a novel “seeking” mechanism that simulates UAV flight behavior by directing the model's attention to informative regions.
Extensive experiments demonstrate that A2Seek-R1 achieves up to a 22.04\% improvement in AP for prediction accuracy and a 13.9\% gain in mIoU for anomaly localization, exhibiting strong generalization across complex environments and out-of-distribution scenarios. Our dataset and code are released at https://2-mo.github.io/A2Seek/.

Dataset URL: https://huggingface.co/datasets/Hayneyday/A2Seek

Code URL: https://2-mo.github.io/A2Seek/

Primary Area: Datasets & Benchmarks for applications in computer vision

Submission Number: 255