Yihao Liu

Even state-of-the-art T2I models falter in generating scientifically accurate illustrations, revealing critical gaps in text-rendering and reasoning capabilities.

Existing image-to-image evaluations miss a critical aspect: whether the output image actually preserves the content of the input.

The medical imaging AI community is being held back by a fragmented data landscape, but a new metadata-driven fusion paradigm offers a path to unlocking the power of foundation models.