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CASENet: Deep Category-Aware Semantic Edge Detection

Yu, Z.; Feng, C.; Liu, M.-Y.; Ramalingam, S.

TR2017-070 May 2017

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

arXiv

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