The encoder-decoder structure has significantly improved performance in many
vision tasks by fusing low-level and high-level feature maps. However, this
approach can hardly extract sufficient context information for pixel-wise
segmentation. In addition, extracting similar low-level features at multiple
scales could lead to redundant information. To tackle these issues, we propose
Subspace Pyramid Fusion Network (SPFNet). Specifically, we combine pyramidal
module and context aggregation module to exploit the impact of
multi-scale/global context information. At first, we construct a Subspace
Pyramid Fusion Module (SPFM) based on Reduced Pyramid Pooling (RPP). Then, we
propose the Efficient Global Context Aggregation (EGCA) module to capture
discriminative features by fusing multi-level global context features. Finally,
we add decoder-based subpixel convolution to retrieve the high-resolution
feature maps, which can help select category localization details. SPFM learns
separate RPP for each feature subspace to capture multi-scale feature
representations, which is more useful for semantic segmentation. EGCA adopts
shuffle attention mechanism to enhance communication across different
sub-features. Experimental results on two well-known semantic segmentation
datasets, including Camvid and Cityscapes, show that our proposed method is
competitive with other state-of-the-art methods.

该研究提出了一种 Subspace Pyramid Fusion Network (SPFNet) 的编码解码结构，旨在提高像素级分割的性能，并采用多尺度 / 全局上下文信息进行上下文聚合，通过实验结果表明该方法在语义分割方面与其他现有技术相当竞争力。