Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic edge detection by nature is an even more challenging multi-label problem. We model the problem such that each edge pixel can be associated with more than one class as they appear in contours or junctions belonging to two or more semantic classes. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features. We then propose a multi-label loss function to supervise the fused activations. We show that our proposed architecture benefits this problem with better performance, and we outperform the current state-of-the-art semantic edge detection methods by a large margin on standard data sets such as SBD and Cityscapes.

本文提出了一种基于ResNet和新的skip-layer架构的端到端深度语义边缘学习体系结构，来解决类别感知的多标签问题， 它将边缘像素与两个或多个语义类别相关联，以捕捉轮廓或接合处的语义信息，并且采用多标签损失函数监督融合活性度， 在SBD和Cityscapes等标准数据集上，我们证明了该方法尤其有效，超越了当前最先进的语义边缘检测方法。

CASENet: 深度类别感知语义边缘检测