{"ID":2887790,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00312","arxiv_id":"2508.00312","title":"GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection","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.","short_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....","url_abs":"https://arxiv.org/abs/2508.00312","url_pdf":"https://arxiv.org/pdf/2508.00312v1","authors":"[\"Suhang Cai\",\"Xiaohao Peng\",\"Chong Wang\",\"Xiaojie Cai\",\"Jiangbo Qian\"]","published":"2025-08-01T04:42:40Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":611467,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2887790,"paper_url":"https://arxiv.org/abs/2508.00312","paper_title":"GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection","repo_url":"https://github.com/Sumutan/GV-VAD.git","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}