A generalist vision encoder must capture semantic, spatial, language-aligned, and action-relevant cues within a unified representation, yet softmax attention underlying today's most capable visual backbones becomes prohibitively expensive at high resolution. A natural attempt to address both challenges is to distill mu...
Jia-Xie Huang, Yue Hu, Xin-Liang-Ma-Ming-Fu Zhu et al.· 0 citations
Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their application in medical settings remains limited by the scarcity of visual question answering (VQA) datasets that capture clinical reasoning and explicit image-text alignm...
Ling-Xuan Hou, Yu-Hua Xie, Yue Hu et al.· 0 citations
A FOundational LLM Trained on ThoughtMed-1M (FOLTMed), a scalable paradigm for advancing research on clinically grounded multimodal LLMs, achieved state-of-the-art performance across 42 medical VQA benchmark datasets, with a macro accuracy of 85.4%, and generated more clinically coherent responses on the ThoughtMed-1M...
Ling-Xuan Hou, Yu-Hua Xie, Yue Hu et al.· 0 citations
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