Context-structured Video Anomaly Detection with Large Vision-Language Models

TL;DR - CSI-VAD is a training-free video anomaly detector that uses large vision-language models to analyze environment, object, and temporal contexts separately. This structured approach improves detection over holistic inference without predefined anomaly prompts or dataset-specific tuning.

• Decomposes videos into three context-specific branches: environment, objects, and time.
• Grounds anomaly judgments solely in visual cues from each context.
• Avoids costly anomaly annotations, handcrafted text prompts, and dataset-specific tuning.
• Outperforms the direct holistic baseline and is competitive with existing methods on UCF-Crime and UBnormal.