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

2026-09-15

AI Summary

为解决视频异常检测中细微差异被忽略的问题，提出Probe-VAD框架，通过直接从冻结的视觉-语言模型中探查严重性偏好来生成连续且敏感的异常评分。

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.

Problem

• Video Anomaly Detection
• Vision-Language Models
• Anomaly Scoring
• Severity Preferences
• Frozen VLM

Innovation

• ordinal binary-probing
• frozen VLM
• severity preference
• continuous anomaly score
• training-free