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.

翻译：以下是一份技术报告，旨在验证所提出的子空间金字塔融合模块（SPFM）在捕获多尺度特征表示方面的有效性，该模块对语义分割任务尤为实用。在本研究中，我们提出了高效洗牌注意力模块（ESAM），通过融合多层次全局上下文特征来重构跳跃连接路径。在包括Camvid和Cityscapes的两个著名语义分割数据集上的实验结果表明了所提方法的有效性。