RANet: Region attention network for semantic segmentation

Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Recent semantic segmentation methods model the relationship between pixels to construct the contextual representations. In this paper, we introduce the Region Attention Network (RANet), a novel attention network for modeling the relationship between object regions. RANet divides the image into object regions, where we select the representative information. In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image. We train the construction of object regions, the selection of the representative regional contents, the configuration of information pathways and the context exchange between pixels, jointly, to improve the segmentation accuracy. We extensively evaluate our method on the challenging segmentation benchmarks, demonstrating that RANet effectively helps to achieve the state-of-the-art results. Code will be available at: https://github.com/dingguo1996/RANet.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems
Pages13927-13938
Number of pages12
Volume33
Publication statusPublished - Dec 2020
Event34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
Duration: 6 Dec 202012 Dec 2020

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference34th Conference on Neural Information Processing Systems, NeurIPS 2020
CityVirtual, Online
Period6/12/2012/12/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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