Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly

arXiv:2405.00181v1 Announce Type: new
Abstract: Video anomaly understanding (VAU) aims to automatically comprehend unusual occurrences in videos, thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization, our focus is on more practicality, prompting us to raise the following crucial questions: "what anomaly occurred?", "why did it happen?", and "how severe is this abnormal event?". In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding …

anomaly arxiv benchmark causation cs.ai cs.cv type understanding video