Hybrid connection network for semantic segmentation

Xiao Liang, Sei Ichiro Kamata

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルTenth International Conference on Digital Image Processing, ICDIP 2018
編集者Jenq-Neng Hwang, Xudong Jiang
出版社SPIE
ISBN(印刷版)9781510621992
DOI
出版ステータスPublished - 2018 1 1
イベント10th International Conference on Digital Image Processing, ICDIP 2018 - Shanghai, China
継続期間: 2018 5 112018 5 14

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
10806
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

Other

Other10th International Conference on Digital Image Processing, ICDIP 2018
CountryChina
CityShanghai
Period18/5/1118/5/14

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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