6 citations · 7 across the 4 of their papers we have counts for
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cs.CV2025
SAVE: Sparse Autoencoder-Driven Visual Information Enhancement for Mitigating Object Hallucination
Sangha Park, Seungryong Yoo, Jisoo Mok +1
Although Multimodal Large Language Models (MLLMs) have advanced substantially, they remain vulnerable to object hallucination caused by language priors and visual information loss.…
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…