CaC presents a new spatiotemporal concentrating reward model for video anomalies, built on a novel large-scale dataset and three-stage training with RL and IoU rewards, claiming 25.7% accuracy gains and 11.7% anomaly reduction.
A survey on video anomaly detection via deep learning: Human, vehicle, and environment
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TouchSafeBench evaluates VLMs on collision grounding, finding best Macro-F1 below 50% and that explicit depth does not yield reliable robot-body contact inference.
Zero-shot MLLMs on ShanghaiTech and CHAD exhibit strong conservative bias with high precision but collapsed recall; class-specific prompts raise peak F1 from 0.09 to 0.64 yet recall remains the bottleneck.
citing papers explorer
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CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating
CaC presents a new spatiotemporal concentrating reward model for video anomalies, built on a novel large-scale dataset and three-stage training with RL and IoU rewards, claiming 25.7% accuracy gains and 11.7% anomaly reduction.
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Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration
TouchSafeBench evaluates VLMs on collision grounding, finding best Macro-F1 below 50% and that explicit depth does not yield reliable robot-body contact inference.
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Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild
Zero-shot MLLMs on ShanghaiTech and CHAD exhibit strong conservative bias with high precision but collapsed recall; class-specific prompts raise peak F1 from 0.09 to 0.64 yet recall remains the bottleneck.