  In this paper, we address the semantic segmentation task with a deep network
that combines contextual features and spatial information. The proposed Cross
Attention Network is composed of two branches and a Feature Cross Attention
(FCA) module. Specifically, a shallow branch is used to preserve low-level
spatial information and a deep branch is employed to extract high-level
contextual features. Then the FCA module is introduced to combine these two
branches. Different from most existing attention mechanisms, the FCA module
obtains spatial attention map and channel attention map from two branches
separately, and then fuses them. The contextual features are used to provide
global contextual guidance in fused feature maps, and spatial features are used
to refine localizations. The proposed network outperforms other real-time
methods with improved speed on the Cityscapes and CamVid datasets with
lightweight backbones, and achieves state-of-the-art performance with a deep
backbone.
