Shih-Ying Yeh — accepted AI-conference papers

2026

ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks

Samin Mahdizadeh Sani, Max Ku, Nima Jamali, Matina Mahdizadeh Sani, Paria Khoshtab, Wei-Chieh Sun +20

ICLR 2026poster

Advances in diffusion, autoregressive, and hybrid models have enabled high-quality image synthesis for tasks such as text-to-image, editing, and reference-guided composition. Yet, existing benchmarks remain limited, either focus on isolated tasks, cover only narrow domains, or provide opaque scores…

Cited by 0 Source ↗code ↗Scholar ↗

2026

TIPO: Text to Image with Text Pre-sampling for Prompt Optimization

Shih-Ying Yeh, YI LI, SangHyun Park, Giyeong Oh, Xuehai Wang, Min Song +2

ICLR 2026poster

TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer, detailed versions. Conceptually, TIPO sa…

Cited by 0 Source ↗Scholar ↗

2024

Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation

SHIH-YING YEH, Yu-Guan Hsieh, Zhidong Gao, Bernard B W Yang, Giyeong Oh, Yanmin Gong

ICLR 2024poster

Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes itself as a leading open-source model in this fast-growing field. However, the intricacies of fine-tuning these models po…

Cited by 146 Source ↗PDF ↗code ↗Scholar ↗