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Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation

arXiv 2204.01278 ↗First posted 4 Apr 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 48 of its 63 references that are in the map. First posted on arXiv 4 Apr 2022.

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References · 48

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36 more
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