Abstract
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.
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URL
https://arxiv.org/abs/2204.01278
