We present a differentiable model that explicitly models boundaries -- including contours, corners and junctions -- using a new mechanism that we call boundary attention. We show that our model provides accurate results even when the boundary signal is very weak or is swamped by noise. Compared to previous classical methods for finding faint boundaries, our model has the advantages of being differentiable; being scalable to larger images; and automatically adapting to an appropriate level of geometric detail in each part of an image. Compared to previous deep methods for finding boundaries via end-to-end training, it has the advantages of providing sub-pixel precision, being more resilient to noise, and being able to process any image at its native resolution and aspect ratio.

翻译：我们提出了一种可微分的模型，该模型通过一种名为边界注意力的新机制显式建模边界——包括轮廓、角点和接合点。研究表明，即使边界信号极其微弱或被噪声淹没，我们的模型仍能提供准确的结果。与以往寻找微弱边界的经典方法相比，本模型具有三大优势：可微分性、可扩展至更大图像、以及自动适应图像各区域所需的几何细节层级。相较于通过端到端训练进行边界检测的现有深度学习方法，本模型具备亚像素精度、更强的抗噪能力，并能以原生分辨率和宽高比处理任意图像。