Edge detection is among the most fundamental vision problems for its role in
perceptual grouping and its wide applications. Recent advances in
representation learning have led to considerable improvements in this area.
Many state of the art edge detection models are learned with fully
convolutional networks (FCNs). However, FCN-based edge learning tends to be
vulnerable to misaligned labels due to the delicate structure of edges. While
such problem was considered in evaluation benchmarks, similar issue has not
been explicitly addressed in general edge learning. In this paper, we show that
label misalignment can cause considerably degraded edge learning quality, and
address this issue by proposing a simultaneous edge alignment and learning
framework. To this end, we formulate a probabilistic model where edge alignment
is treated as latent variable optimization, and is learned end-to-end during
network training. Experiments show several applications of this work, including
improved edge detection with state of the art performance, and automatic
refinement of noisy annotations.

本文提出一个同时进行边缘对齐和学习的概率模型，以解决全卷积神经网络 (FCNs) 边缘学习结构脆弱容易受到标签不对齐的影响的问题，并为噪声注释提供了自动改进机制，改善了边缘检测。