3 citations · 4 across the 5 of their papers we have counts for
5 papers
Dodgersort: Uncertainty-Aware VLM-Guided Human-in-the-Loop Pairwise Ranking
Yujin Park, Haejun Chung, Ikbeom Jang
Pairwise comparison labeling is emerging as it yields higher inter-rater reliability than conventional classification labeling, but exhaustive comparisons require quadratic cost. W…
EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting
Yujin Park, Haejun Chung, Ikbeom Jang
Pairwise comparison is often favored over absolute rating or ordinal classification in subjective or difficult annotation tasks due to its improved reliability. However, exhaustive…
BOLDSimNet: Examining Brain Network Similarity between Task and Resting-State fMRI
Boseong Kim, Debashis Das Chakladar, Haejun Chung +1
Traditional causal connectivity methods in task-based and resting-state functional magnetic resonance imaging (fMRI) face challenges in accurately capturing directed information fl…
Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection
Ik Jun Moon, Junho Moon, Ikbeom Jang
1. Research question: With the growing interest in skin diseases and skin aesthetics, the ability to predict facial wrinkles is becoming increasingly important. This study aims to…
Preoperative Rotator Cuff Tear Prediction from Shoulder Radiographs using a Convolutional Block Attention Module-Integrated Neural Network
Chris Hyunchul Jo, Jiwoong Yang, Byunghwan Jeon +2
Research question: We test whether a plane shoulder radiograph can be used together with deep learning methods to identify patients with rotator cuff tears as opposed to using an M…