Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately, especially for large datasets, and thus the datasets inevitably contain noisy labels. This label-noise issue has been studied extensively for classification, while still remaining under-explored for edge detection. To address the label-noise issue for edge detection, this paper proposes to learn Pixel-level NoiseTransitions to model the label-corruption process. To achieve it, we develop a novel Pixel-wise Shift Learning (PSL) module to estimate the transition from clean to noisy labels as a displacement field. Exploiting the estimated noise transitions, our model, named PNT-Edge, is able to fit the prediction to clean labels. In addition, a local edge density regularization term is devised to exploit local structure information for better transition learning. This term encourages learning large shifts for the edges with complex local structures. Experiments on SBD and Cityscapes demonstrate the effectiveness of our method in relieving the impact of label noise. Codes will be available at github.

翻译：依赖带有像素级标签的大规模训练数据，现有边缘检测方法已取得高性能表现。然而，人工准确标注边缘十分困难，尤其在处理大规模数据集时，因此数据集不可避免地存在噪声标签。该标签噪声问题在分类任务中已获广泛研究，但在边缘检测领域仍缺乏探索。为解决边缘检测中的标签噪声问题，本文提出学习像素级噪声转移（Pixel-level Noise Transitions）来建模标签损坏过程。为此，我们开发了一种新颖的逐像素位移学习（Pixel-wise Shift Learning, PSL）模块，通过将干净标签到噪声标签的转移估计为位移场。借助估计的噪声转移，名为PNT-Edge的模型能够使预测结果拟合干净标签。此外，我们设计了局部边缘密度正则化项，通过利用局部结构信息促进转移学习效果。该项能鼓励对具有复杂局部结构的边缘区域学习更大的位移量。在SBD和Cityscapes数据集上的实验表明，该方法能有效缓解标签噪声的影响。代码将开源至GitHub。