CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection
Existing Video Anomaly Detection (VAD) methods typically rely on task-specific training, leading to strong domain dependency and high training costs. Moreover, most existing methods output only scalar anomaly scores, providing limited insight into why specific events are considered abnormal. Recent advances in Vision-Language Models (VLMs) have enabled both anomaly detection and human-interpretable reasoning. However, many VLM-based approaches still require additional training steps (e.g., instruction tuning or verbalized learning) or external Large Language Models (LLMs), incurring further training costs and inference overhead. To address these challenges, we propose CoReVAD, a contextua...
| Authors | Hyeongmuk Lim, Youngbum Hur |
|---|---|
| Primary category | cs.CV |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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