Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluated
Video anomaly detection (VAD) aims to localize anomalous events by identifying when they occur. A common evaluation pools frame-level anomaly scores across all test videos and measures whether anomalous frames receive higher scores than normal frames (Micro-AUROC). This pooling creates comparisons both within the same video and across different videos. However, the pooled evaluation protocol commonly used in current VAD benchmarks makes Micro-AUROC heavily dependent on cross-video comparisons, which do not directly test whether anomalous frames rank above normal frames within the same video. To quantify this imbalance, we decompose Micro-AUROC into within-video and cross-video comparisons...
| Authors | Inpyo Song, Jangwon Lee |
|---|---|
| Primary category | cs.CV |
| Published | 22 August 2026 |
| Last updated at source | 26 September 2026 |
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