  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 these two problems with two
components: One component which ranks positive pixels over negative pixels, and
the second which promotes high confidence edge pixels to have more label
certainty. We show that RankED outperforms previous studies and sets a new
state-of-the-art on NYUD-v2, BSDS500 and Multi-cue datasets. Code is available
at https://ranked-cvpr24.github.io.
