  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.
