5 citations · 11 across the 9 of their papers we have counts for
9 papers
Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models
Mingyeong Kim, Jungwon Choi, Chaeyun Jang +1
Vision-language models (VLMs) are often deployed on text-only inputs, although they are trained with images. We find that removing the vision modality causes large drops in accurac…
ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection
Deokyun Kim, Jeongjun Lee, Jungwon Choi +6
Detecting missing persons in forest environments remains a challenge, as dense canopy cover often conceals individuals from detection in top-down or oblique aerial imagery typicall…
Stochastic Optimal Control for Continuous-Time fMRI Representation Learning
Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang +4
Learning robust representations from functional magnetic resonance imaging (fMRI) is fundamentally challenged by the temporal irregularity and noise inherent in data from heterogen…
Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
Chaeyun Jang, Hyungi Lee, Jungtaek Kim +1
Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplici…
Stochastic Optimal Control for Diffusion Bridges in Function Spaces
Byoungwoo Park, Jungwon Choi, Sungbin Lim +1
Recent advancements in diffusion models and diffusion bridges primarily focus on finite-dimensional spaces, yet many real-world problems necessitate operations in infinite-dimensio…
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain
Jungwon Choi, Hyungi Lee, Byung-Hoon Kim +1
Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks. However, obtaining extensive la…
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Byung-Hoon Kim, Jungwon Choi, EungGu Yun +3
Graph Transformers have recently been successful in various graph representation learning tasks, providing a number of advantages over message-passing Graph Neural Networks. Utiliz…
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data
Jungwon Choi, Seongho Keum, EungGu Yun +2
Deep neural networks trained on Functional Connectivity (FC) networks extracted from functional Magnetic Resonance Imaging (fMRI) data have gained popularity due to the increasing…
Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification
Seongho Keum, Sanghyun Kim, Soojeong Lee +1
Whole slide image (WSI) classification requires repetitive zoom-in and out for pathologists, as only small portions of the slide may be relevant to detecting cancer. Due to the lac…