Jeongin Kim

I am a graduate student at Ewha Womans University. I am advised by Professor Junhyug Noh, as a member of Practical AI Lab (PAI Lab). Prior to my graduate studies, I earned a bachelor's degree in Electronic Engineering.

My current research focuses on turning foundation models into reliable sources of training signal, enabling segmentation to scale under low-budget settings.

Email / CV / Google Scholar / GitHub

Publications

	Emotion-Aware Multimodal Lightweight Framework for Adaptive Voice Interaction on Edge Device Van-Duc Khuat, Jeongin Kim, Sang-Ho Kim, Yue Cao, Martin Maier, Wansu Lim Knowledge-Based Systems, 2026 [article] A lightweight multimodal framework for emotion-aware voice interaction on edge devices, integrating EEG and facial-expression signals with model compression to support adaptive and efficient on-device responses.
	Anatomy-Structured Hierarchical MIL for Weakly-Supervised Thoracic Disease Detection in Chest X-rays Jeongin Kim, Sohyun Ahn, Seo Young Kang, JaeYi Sung, Soomin Kim, Sungho Cho, Rena Lee, Kwanchang Kim, and Junhyug Noh MICCAI 2026 [article] / [code] ASH-MIL is a weakly-supervised framework for thoracic disease detection in chest X-rays that combines parallel anatomy-structured branches with hierarchical MIL, injecting anatomical priors into decoder cross-attention to enable anatomically grounded localization without bounding box supervision.
	Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation Jeongin Kim, Wonho Bae, YouLee Han, Giyeong Oh, Youngjae Yu, Danica J. Sutherland, Junhyug Noh NeurIPS 2025 [article] / [code] We propose a two-stage active learning pipeline for semantic segmentation under extremely low labeling budgets, which leverages a pre-trained diffusion model for diverse feature extraction and introduces an uncertainty score to select the most informative pixels for annotation.

This page is a fork of Jon Barron's. Thank you for sharing :)