Abstract
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.