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cs.CV2026
MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
Bizhu Wu, Jinheng Xie, Wenting Chen +5
Recent motion-language models unify tasks like comprehension and generation but operate at a coarse granularity, lacking fine-grained understanding and nuanced control over body pa…
cs.CV2026
MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement
Meidan Ding, Jipeng Zhang, Wenxuan Wang +4
Medical Vision-Language Models (MedVLMs) excel at perception tasks but struggle with complex clinical reasoning required in real-world scenarios. While reinforcement learning (RL)…
cs.CV2026
Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models
Shaonan Liu, Guo Yu, Xiaoling Luo +4
Medical Multimodal Large Language Models (Med-MLLMs) require egocentric clinical intent understanding for real-world deployment, yet existing benchmarks fail to evaluate this criti…