Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection

• URL: http://arxiv.org/abs/2609.17211v1
• Date: Tue, 15 Sep 2026 13:59:47 GMT
• Title: Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection
• Abstract summary: Video anomaly detection (VAD) aims to localize anomalous events in untrimmed videos.<n>Existing approaches impose restrictive interfaces between visual understanding and anomaly scoring.<n>We propose textbfProbe-VAD, an ordinal binary-probing framework that directly probes severity preferences from a frozen VLM.
• Score: 34.268676888953046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video anomaly detection (VAD) aims to localize anomalous events in untrimmed videos. Vision-language models (VLMs) provide rich visual understanding for training-free VAD, but existing approaches impose restrictive interfaces between visual understanding and anomaly scoring. Caption-based pipelines compress visual evidence into text, potentially discarding subtle cues, while direct numerical generation forces the model to express its judgment through a small set of predefined scores. Such interfaces can obscure subtle differences in anomaly severity, causing visually distinct clips to receive similar representations or scores and thereby limiting the resolution of anomaly ranking. We propose \textbf{Probe-VAD}, an ordinal binary-probing framework that directly probes severity preferences from a frozen VLM. Given raw video clips, Probe-VAD queries ten ordered severity thresholds and extracts constrained \textit{YES}/\textit{NO} continuation likelihoods. Their normalized preferences form a cumulative severity profile, from which tail evidence is aggregated into a continuous anomaly score, with isotonic projection enforcing ordinal consistency. Experiments on public VAD benchmarks demonstrate superior performance with low computational cost. Probe-VAD provides a simple interface for translating frozen VLM visual understanding into continuous, rank-sensitive anomaly scores without task-specific training or caption-based compression. Code is available at: https://github.com/yvestine/COVAS-VAD.

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