1 citations · 1 across the 3 of their papers we have counts for
4 papers
Rationale-Guided Learning for Multimodal Emotion Recognition
Sujung Oh, Jung Uk Kim, Sangmin Lee
Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most existing approaches fundamentall…
From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation
Evren Çetinkaya, Sangmin Lee, Jung Uk Kim +2
Visual prompting has emerged as a powerful method for adapting pre-trained models to new domains without updating model parameters. However, existing prompting methods typically op…
EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything
Joonhyeon Song, Seohwan Yun, Seongho Yoon +2
This work proposes a novel approach beyond supervised learning for effective pathological image analysis, addressing the challenge of limited robust labeled data. Pathological diag…
CAD: Memory Efficient Convolutional Adapter for Segment Anything
Joohyeok Kim, Joonhyeon Song, Seohwan Yun +2
The Foundation model for image segmentation, Segment Anything (SAM), has been actively researched in various fields since its proposal. Various researches have been proposed to ada…