  This paper introduces an extremely efficient CNN architecture named DFANet
for semantic segmentation under resource constraints. Our proposed network
starts from a single lightweight backbone and aggregates discriminative
features through sub-network and sub-stage cascade respectively. Based on the
multi-scale feature propagation, DFANet substantially reduces the number of
parameters, but still obtains sufficient receptive field and enhances the model
learning ability, which strikes a balance between the speed and segmentation
performance. Experiments on Cityscapes and CamVid datasets demonstrate the
superior performance of DFANet with 8$\times$ less FLOPs and 2$\times$ faster
than the existing state-of-the-art real-time semantic segmentation methods
while providing comparable accuracy. Specifically, it achieves 70.3\% Mean IOU
on the Cityscapes test dataset with only 1.7 GFLOPs and a speed of 160 FPS on
one NVIDIA Titan X card, and 71.3\% Mean IOU with 3.4 GFLOPs while inferring on
a higher resolution image.
