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Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomaly
This work introduces ECVA, a benchmark for Exploring the Causation of Video Anomalies, and proposes AnomShield, a video large language model for reasoning-intensive anomaly understanding that adopts a Chain-of-Thought reasoning paradigm to explicitly determine and extract anomaly-relevant temporal segments.