RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses

arXiv:2403.01795v1 Announce Type: new
Abstract: Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators. Existing solutions address P1 using class-balanced cross-entropy loss and dice loss and P2 by only predicting edges agreed upon by most annotators. In this paper, we propose RankED, a unified ranking-based approach that addresses both the imbalance problem (P1) and the uncertainty problem (P2). RankED tackles …

arxiv cs.cv detection edge losses ranking type uncertainty