Abstract
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.
Abstract (translated)
边缘检测是其在感知分组中的作用及其广泛应用中最基本的视觉问题。代表性学习的最新进展使这一领域有了相当大的改进。利用完全卷积网络（FCN）来学习许多现有技术的边缘检测模型。然而，由于边缘的精细结构，基于FCN的边缘学习往往容易受到未对齐标签的影响。虽然在评估基准中考虑了这样的问题，但在一般边缘学习中并没有明确地解决类似的问题。在本文中，我们表明标签不对齐会导致边缘学习质量显着下降，并通过提出同时边缘对齐和学习框架来解决这个问题。为此，我们制定了一个概率模型，其中边缘对齐被视为潜在变量优化，并在网络训练期间端到端地学习。实验展示了这项工作的几个应用，包括改进的边缘检测和最先进的性能，以及噪声注释的自动细化。
URL
https://arxiv.org/abs/1808.01992
