22 citations · 27 across the 17 of their papers we have counts for
8 papers · 1 filter
Contextualized Visual Personalization in Vision-Language Models
Yeongtak Oh, Sangwon Yu, Junsung Park +3
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 t…
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.…
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…
Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens
Jaihyun Lew, Soohyuk Jang, Jaehoon Lee +6
Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success…
Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection
Saehyung Lee, Jisoo Mok, Sangha Park +3
In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully par…
On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection
Sangha Park, Jisoo Mok, Dahuin Jung +2
Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection…