RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses
This work addresses edge detection in computer vision, which is incremental as it builds on existing ranking-based methods to handle imbalance and uncertainty more effectively.
The paper tackles the problems of class imbalance and label uncertainty in edge detection by proposing RankED, a unified ranking-based loss approach, and shows it outperforms previous methods, achieving new state-of-the-art results on NYUD-v2, BSDS500, and Multi-cue datasets.
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.