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Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation

Authors :
Elhassan, Mohammed A. M.
Yang, Chenhui
Huang, Chenxi
Munea, Tewodros Legesse
Publication Year :
2022

Abstract

The following is a technical report to test the validity of the proposed Subspace Pyramid Fusion Module (SPFM) to capture multi-scale feature representations, which is more useful for semantic segmentation. In this investigation, we have proposed the Efficient Shuffle Attention Module(ESAM) to reconstruct the skip-connections paths by fusing multi-level global context features. Experimental results on two well-known semantic segmentation datasets, including Camvid and Cityscapes, show the effectiveness of our proposed method.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2204.01278
Document Type :
Working Paper