Interactive agents need fine-grained long-video understanding. Current Video-LLMs remain brittle when relevant evidence is subtle or sparse. Fixed frame budgets force rich streams into a few sampled frames and often discard decisive cues. We introduce Video* Bench, a benchmark with 2,212 real-world videos and ten tasks...
Rui-Zhe Chen, Zhi-Ting Fan, Yang Shi et al.· 1 citation
This work presents MedUP, a Med-VLM that natively unifies perception and understanding within a shared token space, and curates UniMed-Train, a 1.84M-instance corpus spanning text-guided segmentation, region-grounded understanding, medical VQA and CoT-based segmentation, and introduces UniMed-Bench for unified evaluati...
Yuan Wang, Hualiang Wang, Yixin Chen et al.· 0 citations
This work develops MedUAG, an end-to-end trained unified medical model that achieves strong performance across a wide array of understanding and generation tasks, establishing a competitive baseline and paving the way for next-generation medical multimodal systems.
Zi-Jie Meng, Yun-Chen Zhang, Hualiang Wang et al.· 0 citations
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