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Fully CapsNet for Semantic Segmentation

  • Su Li
  • Xiangyu Ren
  • Lu Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

Fully convolutional networks (FCNs) are powerful models for semantic segmentation. But convolutional networks fail to perform well in recognizing and parsing images with spatial variation. In this paper, a novel Capsule network called Fully CapsNet is proposed. We introduce Capsule to FCN and improve Equivariance of the neural network in image segmentation. Compared with traditional FCN based networks, a trained Fully CapsNet shows robustness in recognizing image pixels with more or less spatial variation. Each capsule layer is connected by dynamic routing algorithm. The effectiveness of the proposed model is verified through PASCAL VOC. Results show that Fully CapsNet outperforms the FCN in understanding both original images and rotated images.

Keywords

Fully convolutional network Semantic segmentation Capsule network PASCAL VOC 

Notes

Acknowledgement

This research was supported by NSFC (No. 61871074) and Fundamental Research Funds for the Central Universities (ZYGX2018J064).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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