4 papers
Contrastive Order Learning: A General Framework for Ordinal Regression
Chaewon Lee, BeomJun Shim, Kwang Pyo Choi +1
We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. Wh…
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…
Clicks2Line: Using Lines for Interactive Image Segmentation
Chaewon Lee, Chang-Su Kim
For click-based interactive segmentation methods, reducing the number of clicks required to obtain a desired segmentation result is essential. Although recent click-based methods y…
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…