VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree

• URL: http://arxiv.org/abs/2510.22693v2
• Date: Tue, 28 Oct 2025 16:57:22 GMT
• Title: VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree
• Authors: Wenlong Li, Yifei Xu, Yuan Rao, Zhenhua Wang, Shuiguang Deng,
• Abstract summary: Video anomaly detection (VAD) focuses on identifying anomalies in videos.<n>We propose VADTree that utilizes a Hierarchical Granularity Tree structure for flexible sampling in VAD.<n>VADTree achieves state-of-the-art performance in training-free settings while drastically reducing the number of sampled video segments.
• Score: 21.721087343852158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection (VAD) focuses on identifying anomalies in videos. Supervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves 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 VADTree that utilizes a Hierarchical Granularityaware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries. Specifically, VADTree decomposes the video into generic event nodes based on boundary confidence, and performs adaptive coarse-fine hierarchical structuring and redundancy removal to construct the HGTree. Then, the multi-dimensional priors are injected into the visual language models (VLMs) to enhance the node-wise anomaly perception, and anomaly reasoning for generic event nodes is achieved via large language models (LLMs). Finally, an inter-cluster node correlation method is used to integrate the multi-granularity anomaly scores. Extensive experiments on three challenging datasets demonstrate that VADTree achieves state-of-the-art performance in training-free settings while drastically reducing the number of sampled video segments. The code will be available at https://github.com/wenlongli10/VADTree.

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