Hybrid connection network for semantic segmentation

Xiao Liang, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In recent years, deep convolutional neural networks like ResNet and DenseNet with short cut connected to each layer can be more accurate and easier to train. Although deep convolutional neural networks show their strength in many computer vision tasks, there is still a challenge to get more precise per-pixel prediction for semantic image segmentation task via deep convolutional neural networks. In this paper, we propose a hybrid connection network architecture for semantic segmentation which consists of an encoder network for extracting different scale feature maps and a decoder network for recovering extracted feature maps with the resolution of the input image. This architecture includes several skip connection paths between encoder and decoder. The paths help to fuse both localization information and global information. We show that our architecture can be quickly trained end-to-end without pre-training on an additional dataset and performs comparable results on semantic segmentation benchmark datasets such as PASCAL VOC 2012.

Original languageEnglish
Title of host publicationTenth International Conference on Digital Image Processing, ICDIP 2018
EditorsJenq-Neng Hwang, Xudong Jiang
ISBN (Print)9781510621992
Publication statusPublished - 2018
Event10th International Conference on Digital Image Processing, ICDIP 2018 - Shanghai, China
Duration: 2018 May 112018 May 14

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Other10th International Conference on Digital Image Processing, ICDIP 2018


  • Computer vision
  • DenseNet
  • Fully convolutional network
  • ResNet
  • Semantic image segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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