Semantic segmentation network with multi-path structure, attention reweighting and multi-scale encoding-Reference-Cited by-同舟云学术

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Semantic segmentation network with multi-path structure, attention reweighting and multi-scale encoding

Published:2022-01-26 Issue:2 Volume:39 Page:597-608
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Lin Zhongkang,Sun Wei^ORCID,Tang Bo,Li Jinda,Yao Xinyuan,Li Yu

Funder

National Natural Science Foundation of China

Hubei Provincial Department of Education

WUST National Defense Pre-research Foundation

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-021-02360-7.pdf

Reference58 articles.

1. Liu, F., et al.: Structural feature learning-based unsupervised semantic segmentation of synthetic aperture radar image. J. Appl. Remote Sens. 13(1), 014501 (2019)

2. Wang, D., Han, M.: SA-U-Net++: SAR marine floating raft aquaculture identification based on semantic segmentation and ISAR augmentation. J. Appl. Remote Sens. 15(1), 016505 (2021)

3. Liu, Y., et al.: Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields. J. Appl. Remote Sens. 13(1), 016501 (2019)

4. Wang, Y., Xiao, S.: Learning multiscale spatial context for three-dimensional point cloud semantic segmentation. J Electron Imag 29(6), 063005 (2020)

5. Ku, T., et al.: SHREC 2020: 3D point cloud semantic segmentation for street scenes. Comput. Graph. 93, 13–24 (2020)

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