Jiuxiang Gu

Visual text grounding provides interpretable evidence for document question answering. Due to the complex layouts and mixed visual-text contents in text-rich images, effective visual text grounding requires strong visual and spatial reasoning to localize multiple referenced regions. Existing mult...

Recent text-to-image models achieve impressive visual quality but still face challenges in precise controllability, balancing multimodal inputs, and high training cost for multimodal image generation. To address these limitations, we propose MENTOR, an autoregressive (AR) framework with a two-sta...