46 citations · 61 across the 6 of their papers we have counts for
6 papers
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification
Sion An, Myeongkyun Kang, Soopil Kim +3
Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variab…
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…
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs
Dongkyu Won, Euijin Jung, Sion An +2
Recently, Self-supervised learning methods able to perform image denoising without ground truth labels have been proposed. These methods create low-quality images by adding random…
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation
Soopil Kim, Sion An, Philip Chikontwe +1
Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success…
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification
Sion An, Soopil Kim, Philip Chikontwe +1
Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) signals, in particular motor imagery (MI) data have received a lot of attention and show the potential towards…