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Semantic Edge Detection with Diverse Deep Supervision (1804.02864v5)

Published 9 Apr 2018 in cs.CV

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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Authors (6)
  1. Yun Liu (213 papers)
  2. Ming-Ming Cheng (185 papers)
  3. Deng-Ping Fan (88 papers)
  4. Le Zhang (180 papers)
  5. JiaWang Bian (8 papers)
  6. Dacheng Tao (830 papers)
Citations (93)

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