邊界注意力：學習在任何解析度下尋找微弱邊界

摘要

我們提出了一個可微分模型，明確地模擬邊界，包括輪廓、角落和交界，使用我們稱之為邊界關注的新機制。我們展示了即使邊界信號非常微弱或被噪音淹沒，我們的模型也能提供準確的結果。與以往用於尋找微弱邊界的古典方法相比，我們的模型具有以下優勢：可微分性；可擴展至更大的圖像；並自動適應圖像各部分的適當幾何細節水平。與以往通過端到端訓練尋找邊界的深度方法相比，它具有提供次像素精度、更具抗噪能力，並能夠以原生解析度和寬高比處理任何圖像的優勢。

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.

邊界注意力：學習在任何解析度下尋找微弱邊界

Boundary Attention: Learning to Find Faint Boundaries at Any Resolution

摘要

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