1 citations · 1 across the 4 of their papers we have counts for
5 papers
MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation
Junjie Yang, Yuhao Yan, Gang Wu +12
Medical vision-language models (VLMs) are increasingly expected to support clinical workflows through diagnostic text and relevant medical images. However, current medical visual b…
CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching
Chen Chen, Pengsheng Guo, Liangchen Song +7
Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained…
MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Yanghao Li, Rui Qian, Bowen Pan +24
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from…
DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to Image Generation
Chen Chen, Rui Qian, Wenze Hu +8
In this work, we empirically study Diffusion Transformers (DiTs) for text-to-image generation, focusing on architectural choices, text-conditioning strategies, and training protoco…
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models
Cristina N. Vasconcelos, Abdullah Rashwan, Austin Waters +22
We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable trainin…