arXiv Computer Vision
A VLM Answer Is Not an Anomaly Score: Rank Compression Across Image and Video Anomaly Detection
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A VLM Answer Is Not an Anomaly Score: Rank Compression in Training-Free Video Anomaly Detection
The paper investigates how vision‑language models (VLMs) can be used for training‑free video anomaly detection (VAD) by answering questions about video segments. It shows that the way a VLM’s output is converted into an anomaly score—either by taking only the most likely answer (generated readout) or by using the full probability distribution (probability readout)—has a significant impact on ranking performance. Across four large VLMs, the probability readout consistently outperforms the generated readout, achieving 5–13 point gains in AUROC or AP, because the generated readout compresses the rank order into only a few distinct scores.
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