6 papers
STAG: Structural Test-time Alignment of Gradients for Online Adaptation
Juhyeon Shin, Yujin Oh, Jonghyun Lee +5
Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose Structura…
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Saehyung Lee, Seunghyun Yoon, Trung Bui +2
Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that existing hallucination detection m…
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context
Sangwon Yu, Ik-hwan Kim, Jongyoon Song +3
Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often…
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…
Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach
Saehyung Lee, Sangwon Yu, Junsung Park +2
In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the gen…