Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluated

arXiv:2608.21854 · cs.CV · Submitted 2026-08-22 · Read on arXiv

cs.CV

Submitted: 2026-08-22

Updated: 2026-09-26

Terminology

• Benchmark — 42× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 13× in this paper
• Alignment — 6× in this paper · explained in 1 episode
• Transformer — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Generative AI — 3× in this paper · explained in 2 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Sources

• Limitations of Pinned AUC for Measuring Unintended Bias
• MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
• Rethinking Metrics and Benchmarks of Video Anomaly Detection
• Bounding-Box Trajectories Matter for Video Anomaly Detection
• Rethinking Open-World Video Anomaly Detection: Diagnosing Definition Blindness

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