3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
Chaewon Lee, Seon-Ho Lee, Chang-Su Kim
Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption. In this paper, we…
cs.CV2024
MFP: Making Full Use of Probability Maps for Interactive Image Segmentation
Chaewon Lee, Seon-Ho Lee, Chang-Su Kim
In recent interactive segmentation algorithms, previous probability maps are used as network input to help predictions in the current segmentation round. However, despite the utili…
cs.CV2022★ 3 cited
Moving Window Regression: A Novel Approach to Ordinal Regression
Nyeong-Ho Shin, Seon-Ho Lee, Chang-Su Kim
A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (-rank), which is a new ord…