边界注意力：学习在任意分辨率下找到微弱边界

摘要

我们提出了一种可微分模型，明确地建模边界，包括轮廓、角点和交界，使用我们称之为边界注意力的新机制。我们展示了即使边界信号非常微弱或被噪声淹没，我们的模型也能提供准确的结果。与以往用于发现微弱边界的经典方法相比，我们的模型具有以下优势：可微分性；可扩展到更大的图像；并且能够自动适应图像各部分的适当几何细节水平。与以往通过端到端训练来发现边界的深度方法相比，它具有提供亚像素精度、更具噪声韧性，并且能够以其原生分辨率和纵横比处理任何图像的优势。

English

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.

边界注意力：学习在任意分辨率下找到微弱边界

Boundary Attention: Learning to Find Faint Boundaries at Any Resolution

摘要

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