用生成视频增强弱监督异常检测，低成本提升模型性能
GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection
- 用文本控制的视频生成模型创造逼真合成视频用于训练
- 在UCF-Crime数据集上超越现有最先进方法
- 适合资源有限但需提升异常检测能力的研究者
视频异常检测（VAD）在智能监控等公共安全应用中至关重要。然而，真实世界异常事件稀少、不可预测且标注成本高，导致难以构建大规模数据集，限制了现有模型的性能与泛化能力。为此，我们提出一种生成视频增强的弱监督视频异常检测框架（GV-VAD），利用文本条件视频生成模型生成语义可控且物理合理的合成视频，以低成本扩充训练数据。同时，采用合成样本损失缩放策略，有效控制生成样本对训练的影响。实验表明，该框架在UCF-Crime数据集上优于现有最先进方法。代码已开源：https://github.com/Sumutan/GV-VAD.git。
原文摘要 · Abstract (English)
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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