无需训练，用大模型直接判断视频异常并给出解释
CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection
- 仅用一个冻结的视觉语言模型生成异常评分与时间描述
- 在UCF-Crime和XD-Violence上表现优于其他免训练方法
- 适合需要可解释性且不想耗时训练的场景
现有视频异常检测方法通常依赖特定任务训练，导致领域依赖性强且训练成本高。多数方法仅输出标量异常分数，难以说明事件为何异常。尽管视觉语言模型（VLM）已能实现异常检测与人类可理解推理，但许多基于VLM的方法仍需额外训练步骤（如指令微调或语义学习）或外部大语言模型，增加训练成本与推理开销。为此，我们提出CoReVAD，一种无需训练的上下文推理框架，仅使用单个冻结的VLM。CoReVAD直接从VLM生成异常分数与时间描述。为减轻生成输出中的噪声，引入基于局部视觉-文本对齐的局部响应清洗（LRC）模块；同时通过softmax优化、高斯平滑与位置加权融合全局时间上下文与进展信息。在UCF-Crime与XD-Violence数据集上的实验表明，CoReVAD在免训练方法中表现优异，同时提供可靠且可解释的推理。官方代码已开源：https://github.com/Muk-00/CoReVAD
原文摘要 · Abstract (English)
Existing Video Anomaly Detection (VAD) methods typically rely on task-specific training, leading to strong domain dependency and high training costs. Moreover, most existing methods output only scalar anomaly scores, providing limited insight into why specific events are considered abnormal. Recent advances in Vision-Language Models (VLMs) have enabled both anomaly detection and human-interpretable reasoning. However, many VLM-based approaches still require additional training steps (e.g., instruction tuning or verbalized learning) or external Large Language Models (LLMs), incurring further training costs and inference overhead. To address these challenges, we propose CoReVAD, a contextual reasoning framework for training-free video anomaly detection that operates with a single frozen VLM. CoReVAD directly generates anomaly scores and temporal descriptions from the VLM. To mitigate noise in generative outputs, we introduce a Local Response Cleaning (LRC) module based on local vision-text alignment. Furthermore, global temporal context and progression are incorporated through softmax-based refinement, Gaussian smoothing, and position weighting. Experiments on UCF-Crime and XD-Violence demonstrate that CoReVAD achieves competitive performance among training-free methods while providing reliable and interpretable explanations. Our official code is available at: https://github.com/Muk-00/CoReVAD
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