Abstract
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at \url{this https URL}
Abstract (translated)
深度学习基于的边缘检测方法通常依赖于多个标注者提供的像素级标签。现有的方法使用简单的投票过程将多个标注者提供的标注合并,而忽略边缘固有的歧义和标注者的标注偏见。在本文中,我们提出了一种新的不确定性意识到边缘检测器(UAED),该检测器使用不确定性来研究多种标注者的 Subjectivity 和歧义。具体而言,我们首先将确定性标签空间转换为可学习的高斯分布,其方差衡量不同标注者之间的歧义程度。然后,我们将学到的方差视为预测的边缘地图估计的不确定性,而高不确定性的像素可能被视为边缘检测的硬样本。因此,我们设计了一种自适应权重损失,以强调从高不确定性像素的学习,这有助于网络逐渐集中到重要的像素。 UAED 可以与各种编码器和解码器骨架相结合,广泛实验表明,UAED 在多个边缘检测基准上表现出卓越的性能。源代码可以在 \url{this https URL} 获取。
URL
https://arxiv.org/abs/2303.11828