3 papers
cs.CV2025
Guiding What Not to Generate: Automated Negative Prompting for Text-Image Alignment
Sangha Park, Eunji Kim, Yeongtak Oh +2
Despite substantial progress in text-to-image generation, achieving precise text-image alignment remains challenging, particularly for prompts with rich compositional structure or…
cs.CV2025
RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models
Yeongtak Oh, Dohyun Chung, Juhyeon Shin +4
Recent multi-modal large language models (MLLMs) often struggle to generate personalized image captions, even when trained on high-quality captions. In this work, we observe that s…
cs.CV2024
Style-Friendly SNR Sampler for Style-Driven Generation
Jooyoung Choi, Chaehun Shin, Yeongtak Oh +3
Recent text-to-image diffusion models generate high-quality images but struggle to learn new, personalized styles, which limits the creation of unique style templates. In style-dri…