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arXiv · 1804.02864

Semantic Edge Detection with Diverse Deep Supervision

Yun Liu·Ming-Ming Cheng·Deng-Ping Fan·Le Zhang·JiaWang Bian·Dacheng Tao
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Abstract

Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. Our motivation comes from the hypothesis that such distinct targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. To this end, we propose a novel fully convolutional neural network using diverse deep supervision (DDS) within a multi-task framework where bottom layers aim at generating category-agnostic edges, while top layers are responsible for the detection of category-aware semantic edges. To overcome the hypothesized supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated on SBD and Cityscapes datasets.

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Yun Liu, Ming-Ming Cheng, Deng-Ping Fan, Le Zhang, JiaWang Bian, Dacheng Tao. 2018-04-09. Semantic Edge Detection with Diverse Deep Supervision. https://arxiv.org/abs/1804.02864

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