CaC is a hierarchical spatiotemporal concentrating reward model for video anomalies that reports 25.7% accuracy gains on fine-grained benchmarks and 11.7% anomaly reduction in generated videos via a new dataset and GRPO training with temporal/spatial IoU rewards.
the erroneous object identified by the AI model must refer to the same entity or the same region as the human annotation
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CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating
CaC is a hierarchical spatiotemporal concentrating reward model for video anomalies that reports 25.7% accuracy gains on fine-grained benchmarks and 11.7% anomaly reduction in generated videos via a new dataset and GRPO training with temporal/spatial IoU rewards.