  In this paper, we focus on designing effective method for fast and accurate
scene parsing. A common practice to improve the performance is to attain high
resolution feature maps with strong semantic representation. Two strategies are
widely used -- atrous convolutions and feature pyramid fusion, are either
computation intensive or ineffective. Inspired by the Optical Flow for motion
alignment between adjacent video frames, we propose a Flow Alignment Module
(FAM) to learn Semantic Flow between feature maps of adjacent levels, and
broadcast high-level features to high resolution features effectively and
efficiently. Furthermore, integrating our module to a common feature pyramid
structure exhibits superior performance over other real-time methods even on
light-weight backbone networks, such as ResNet-18. Extensive experiments are
conducted on several challenging datasets, including Cityscapes, PASCAL
Context, ADE20K and CamVid. Especially, our network is the first to achieve
80.4\% mIoU on Cityscapes with a frame rate of 26 FPS. The code is available at
\url{https://github.com/lxtGH/SFSegNets}.
