VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree
Wenlong Li, Yifei Xu, Yuan Rao, Zhenhua Wang, Shuiguang Deng
Video anomaly detection (VAD) focuses on identifying anomalies in videos. Supervised methods demand substantial in-domain training data and fail to deliver clear explanation for anomalies. In contrast, training-free methods leverage the knowledge reserve and language interactivity of large pre-trained models to detect anomalies. However, the current fixed-length temporal window sampling approaches struggle to accurately capture anomalies  with varying temporal spans.Therefore, we propose \textbf{\methodshort}~that utilizes a Hierarchical Granularity-aware Tree (HGTree) structure for adaptive sampling VAD. \methodshort~ leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries.Specifically, \methodshort~first decomposes the video into an HGTree based on generic event nodes using boundary confidence, and performs adaptive coarse-fine stratification and redundancy removal. Then, the multi-dimensional priors are injected into the vision-language models (VLMs) to enhance the abnormal perception of the node-wise video description, and robust anomaly reasoning is achieved for generic event nodes based on the large language models (LLMs).Finally, an intra-cluster correlation method is used to integrate the multi-granular anomaly scores. Extensive experiments on UCF-Crime and XD-Violence datasets demonstrate that  \methodshort~achieves state-of-the-art performance in training-free settings while drastically reducing the amount of video samples. Our code is publicly available at\url{https://anonymous.4open.science/r/\methodshort-6E11/}.