articleOct 1, 2019Closed access

Asymmetric Non-Local Neural Networks for Semantic Segmentation

Huazhong University of Science and Technology · University of Oxford

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Abstract

The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, which has two prominent components: Asymmetric Pyramid Non-local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB). APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance. AFNB is adapted from APNB to fuse the features of different levels under a sufficient consideration of long range dependencies and thus considerably improves the performance.…

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Authors

5

Topics & keywords

Keywords
  • Computer science
  • Segmentation
  • Block (permutation group theory)
  • Pyramid (geometry)
  • Fuse (electrical)
  • Computation
  • Code (set theory)
  • Set (abstract data type)
UN Sustainable Development Goals
  • Sustainable cities and communities
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