AI RESEARCH
Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks
arXiv CS.CV
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ArXi:2511.19474v4 Announce Type: replace Automatically detecting abnormal events in videos is crucial for modern autonomous systems, yet existing Video Anomaly Detection (VAD) benchmarks lack the scene diversity, balanced anomaly coverage, and temporal complexity needed to reliably assess real-world performance. Meanwhile, the community is increasingly moving toward Video Anomaly Understanding (VAU), which requires deeper semantic and causal reasoning but remains difficult to benchmark due to the heavy manual annotation effort it demands. In this paper, we