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 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 this https URL.
Abstract (translated)
在图像中检测边缘存在以下问题：（P1）正负类别之间的严重不平衡；（P2）由于不同注释者之间的分歧，导致标签不确定性。现有的解决方案通过类别平衡交叉熵损失和Dice损失来解决（P1）问题，而通过仅预测由大多数注释者达成一致的边缘来解决（P2）问题。在本文中，我们提出 RankED，一种基于统一排名的方法，解决了（P1）和（P2）问题。RankED 使用两个组件来解决这些问题：一个组件对负类像素进行排名，另一个组件通过促进高置信度边缘像素具有更多标签确定性来解决（P2）问题。我们证明了 RankED 超越了以前的研究，并在 NYUD-v2、BSDS500 和 Multi-cue 数据集上取得了最先进的水平。代码可在此处下载：https://url.com/
URL
https://arxiv.org/abs/2403.01795
