# Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation

Source: [Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa71641e209131cea511b2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:05:39.468Z

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

Published: 2022-04-04T07:04:50.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.01278>)

[PDF](<https://arxiv.org/pdf/2204.01278.pdf>)