Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Prompting
This work addresses video anomaly detection for surveillance applications, offering an incremental improvement through fine-grained prompting.
The paper tackled the problem of adapting frozen vision-language models for video anomaly detection by addressing overly abstract prompts, proposing ASK-Hint, a structured prompting framework that improves AUC on datasets like UCF-Crime and XD-Violence, achieving state-of-the-art performance.
Prompting has emerged as a practical way to adapt frozen vision-language models (VLMs) for video anomaly detection (VAD). Yet, existing prompts are often overly abstract, overlooking the fine-grained human-object interactions or action semantics that define complex anomalies in surveillance videos. We propose ASK-Hint, a structured prompting framework that leverages action-centric knowledge to elicit more accurate and interpretable reasoning from frozen VLMs. Our approach organizes prompts into semantically coherent groups (e.g. violence, property crimes, public safety) and formulates fine-grained guiding questions that align model predictions with discriminative visual cues. Extensive experiments on UCF-Crime and XD-Violence show that ASK-Hint consistently improves AUC over prior baselines, achieving state-of-the-art performance compared to both fine-tuned and training-free methods. Beyond accuracy, our framework provides interpretable reasoning traces towards anomaly and demonstrates strong generalization across datasets and VLM backbones. These results highlight the critical role of prompt granularity and establish ASK-Hint as a new training-free and generalizable solution for explainable video anomaly detection.