Xiaohong Liu

Finally, a blind face restoration method that doesn't just hallucinate details, but lets you precisely control facial attributes via text prompts while maintaining high fidelity.

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.

Achieve state-of-the-art single image reflection removal by explicitly guiding a diffusion model with spatial intensity and high-frequency priors derived directly from the input image.

Embodied agents can now collaboratively reason about space and manipulate objects in the real world, thanks to a new reinforcement learning approach that fuses their egocentric viewpoints into a world-centric understanding.

LLMs can now predict other LLMs' performance with 14% higher accuracy, even when only seeing one or two data points, by blending statistical priors with reasoning.

LLMs maintain a positive attitude even when losing in adversarial board games, yet their gameplay reveals surprising instability in skill execution.