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CARNet: Context Attention Refine Network for Semantic Segmentation

Recent works in semantic segmentation have focused on exploiting richer context through attention mechanisms or multi-scale fusion. Most of them ignore the low-level details, which greatly improve the recognition of pixels in the edge of the object. In this paper, we improve the quality of feature maps from two aspects: muti-level features fusion and global context encoding. A Context Attention Unit (CAU) is proposed to aggregate context information. The CAU utilizes self-attention model to enhance the semantic representation of high-level features. To make effective use of the different characteristics of multi-level feature maps, we further present a Gated Fuse Unit (GFU). A gate is calculated to control the information transfer of the feature maps of the adjacent layers. Base on these two units, a network with encoder-decoder structure is designed to gradually refine the feature map. Then the refined feature map generates the final segmentation prediction. We conduct extensive experiments on popular semantic segmentation benchmarks including Cityscapes and PASCAL VOC, which shows the effectiveness of our method.