6 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following
Myeongkyun Kang, Soopil Kim, Xiaoxiao Li +1
Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from visual instruction tuning, which fi…
Model Agnostic Preference Optimization for Medical Image Segmentation
Yunseong Nam, Jiwon Jang, Dongkyu Won +2
Preference optimization offers a scalable supervision paradigm based on relative preference signals, yet prior attempts in medical image segmentation remain model-specific and rely…
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Soopil Kim, Sion An, Philip Chikontwe +4
Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately su…
Uncertainty-Aware Semi-Supervised Few Shot Segmentation
Soopil Kim, Philip Chikontwe, Sang Hyun Park
Few shot segmentation (FSS) aims to learn pixel-level classification of a target object in a query image using only a few annotated support samples. This is challenging as it requi…
A Meta-Learning Approach for Medical Image Registration
Heejung Park, Gyeong Min Lee, Soopil Kim +4
Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often requir…