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Yeongtak Oh

3 accepted papers

2026

Contextualized Visual Personalization in Vision-Language Models

ICML 2026poster

Despite recent progress in vision-language models (VLMs), existing approaches often fail to generate personalized responses based on the user's specific experiences, as they lack the ability to associate visual inputs with a user’s accumulated visual-textual context. We newly formalize this challeng…

Cited by 0SourceScholar
2025

RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models

NeurIPS 2025poster

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 such limitations persist in existing post-training-based MLLM personalization methods. Specifically, despite being post-t…

Cited by 0SourcecodeScholar
2024

Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation

ECCV 2024poster

"Test-time adaptation (TTA) addresses the unforeseen distribution shifts occurring during test time. In TTA, performance, memory consumption, and time consumption are crucial considerations. A recent diffusion-based TTA approach for restoring corrupted images involves image-level updates. However, u…