A Pyramid Attention Network(PAN) is proposed to exploit the impact of global
contextual information in semantic segmentation. Different from most existing
works, we combine attention mechanism and spatial pyramid to extract precise
dense features for pixel labeling instead of complicated dilated convolution
and artificially designed decoder networks. Specifically, we introduce a
Feature Pyramid Attention module to perform spatial pyramid attention structure
on high-level output and combining global pooling to learn a better feature
representation, and a Global Attention Upsample module on each decoder layer to
provide global context as a guidance of low-level features to select category
localization details. The proposed approach achieves state-of-the-art
performance on PASCAL VOC 2012 and Cityscapes benchmarks with a new record of
mIoU accuracy 84.0% on PASCAL VOC 2012, while training without COCO dataset.

使用金字塔注意力网络 (PAN) 进行语义分割，通过特征金字塔注意力模块和全局注意力上采样模块提取精确的密集特征，优于现有方法，可达到 PASCAL VOC 2012 和 Cityscapes 数据集的最高精度 mIoU 84.0%。