Video anomaly detection (VAD) aims to temporally locate abnormal events in a
video. Existing works mostly rely on training deep models to learn the
distribution of normality with either video-level supervision, one-class
supervision, or in an unsupervised setting. Training-based methods are prone to
be domain-specific, thus being costly for practical deployment as any domain
change will involve data collection and model training. In this paper, we
radically depart from previous efforts and propose LAnguage-based VAD (LAVAD),
a method tackling VAD in a novel, training-free paradigm, exploiting the
capabilities of pre-trained large language models (LLMs) and existing
vision-language models (VLMs). We leverage VLM-based captioning models to
generate textual descriptions for each frame of any test video. With the
textual scene description, we then devise a prompting mechanism to unlock the
capability of LLMs in terms of temporal aggregation and anomaly score
estimation, turning LLMs into an effective video anomaly detector. We further
leverage modality-aligned VLMs and propose effective techniques based on
cross-modal similarity for cleaning noisy captions and refining the LLM-based
anomaly scores. We evaluate LAVAD on two large datasets featuring real-world
surveillance scenarios (UCF-Crime and XD-Violence), showing that it outperforms
both unsupervised and one-class methods without requiring any training or data
collection.

视频异常检测 (VAD) 旨在暂时定位视频中的异常事件。本文提出了一种名为 LAnguage-based VAD (LAVAD) 的方法，利用预训练的大型语言模型 (LLMs) 和现有的视觉 - 语言模型 (VLMs) 来处理 VAD，通过生成每个测试视频的文字描述，设计了一种启动机制，将 LLMs 转化为有效的视频异常检测器，并结合跨模态相似度进行清理和改善 LLMs 的基于异常评分的方法。在两个具有真实监控场景的大型数据集 (UCF-Crime 和 XD-Violence) 上评估 LAVAD，结果显示它在不需要任何训练或数据收集的情况下优于无监督和单类方法。