FAFNet: Fully aligned fusion network for RGBD semantic segmentation based on hierarchical semantic flows-Reference-Cited by-同舟云学术

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FAFNet: Fully aligned fusion network for RGBD semantic segmentation based on hierarchical semantic flows

Published:2022-08-25 Issue:1 Volume:17 Page:32-41
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Chen Jiazhou¹^ORCID,Zhan Yangfan¹,Xu Yanghui¹,Pan Xiang¹

Affiliation:

1. School of Computer Science and Technology Zhejiang University of Technology Hangzhou P.R.China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12614

Reference42 articles.

1. Long J. Shelhamer E. Darrell T.:Fully convolutional networks for semantic segmentation. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp.3431–3440.IEEE Piscataway(2015)

2. Zhao H. Shi J. Qi X. Wang X. Jia J.:Pyramid scene parsing network. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp.2881–2890.IEEE Piscataway(2017)

3. Chen L.C. Papandreou G. Schroff F. Adam H.:Rethinking atrous convolution for semantic image segmentation.arXiv preprint arXiv:170605587(2017)

4. Li X. You A. Zhu Z. Zhao H. Yang M. Yang K. et al.:Semantic flow for fast and accurate scene parsing. In:Proceedings of European Conference on Computer Vision (ECCV) pp.775–793.Springer Berlin(2020)

5. Semantic image segmentation using an improved hierarchical graphical model

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