GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection

• URL: http://arxiv.org/abs/2508.00312v1
• Date: Fri, 01 Aug 2025 04:42:40 GMT
• Title: GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection
• Authors: Suhang Cai, Xiaohao Peng, Chong Wang, Xiaojie Cai, Jiangbo Qian,
• Abstract summary: Video anomaly detection (VAD) plays a critical role in public safety applications such as intelligent surveillance.<n>We propose a generative video-enhanced weakly-supervised VAD framework to produce semantically controllable and physically plausible synthetic videos.<n>The proposed framework outperforms state-of-the-art methods on UCF-Crime datasets.
• Score: 6.09434007746295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection (VAD) plays a critical role in public safety applications such as intelligent surveillance. However, the rarity, unpredictability, and high annotation cost of real-world anomalies make it difficult to scale VAD datasets, which limits the performance and generalization ability of existing models. To address this challenge, we propose a generative video-enhanced weakly-supervised video anomaly detection (GV-VAD) framework that leverages text-conditioned video generation models to produce semantically controllable and physically plausible synthetic videos. These virtual videos are used to augment training data at low cost. In addition, a synthetic sample loss scaling strategy is utilized to control the influence of generated synthetic samples for efficient training. The experiments show that the proposed framework outperforms state-of-the-art methods on UCF-Crime datasets. The code is available at https://github.com/Sumutan/GV-VAD.git.

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