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

• URL: http://arxiv.org/abs/2607.19077v1
• Date: Tue, 21 Jul 2026 13:08:14 GMT
• Title: Context-structured Video Anomaly Detection with Large Vision-Language Models
• Abstract summary: We present CSI-VAD, a training-free video anomaly detector that identifies abnormal events across diverse contexts.<n>The key idea is to decompose each video into three distinct contexts (environment, objects, time) and perform context-specific inference in separate branches.<n>Experiments on UCF-Crime and UBnormal show that CSI-VAD consistently improves over the direct holistic baseline.
• Score: 30.02492464154922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training video anomaly detectors is challenging due to the difficulty and cost of annotating diverse and rare abnormal events. Although recent large vision-language models enable training-free inference, existing approaches mostly rely on holistic inference over sampled video and may miss context-specific anomaly cues. In this paper, we present CSI-VAD, a training-free video anomaly detector that identifies abnormal events across diverse contexts. The key idea is to decompose each video into three distinct contexts (environment, objects, time) and perform context-specific inference in separate branches. Because we ground anomaly judgments solely in context-specific visual cues, we do not require predefined text prompts describing abnormal events or dataset-specific tuning. Experiments on UCF-Crime and UBnormal show that CSI-VAD consistently improves over the direct holistic baseline and achieves competitive performance against existing methods, showing the advantage of structured context decomposition for training-free video anomaly detection.

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