1 citations · 1 across the 5 of their papers we have counts for
5 papers
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu +4
Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state…
Memorization in 3D Shape Generation: An Empirical Study
Shu Pu, Boya Zeng, Kaichen Zhou +2
Generative models are increasingly used in 3D vision to synthesize novel shapes, yet it remains unclear whether their generation relies on memorizing training shapes. Understanding…
Judge Anything: MLLM as a Judge Across Any Modality
Shu Pu, Yaochen Wang, Dongping Chen +10
Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., images, audio, video) poses signifi…
Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination
Haojie Zheng, Tianyang Xu, Hanchi Sun +3
Multimodal large language models (MLLMs) have advanced the integration of visual and linguistic modalities, establishing themselves as the dominant paradigm for visual-language tas…
Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment
Dongping Chen, Ruoxi Chen, Shu Pu +8
Many real-world user queries (e.g. "How do to make egg fried rice?") could benefit from systems capable of generating responses with both textual steps with accompanying images, si…