  Real-time semantic segmentation is playing a more important role in computer
vision, due to the growing demand for mobile devices and autonomous driving.
Therefore, it is very important to achieve a good trade-off among performance,
model size and inference speed. In this paper, we propose a Channel-wise
Feature Pyramid (CFP) module to balance those factors. Based on the CFP module,
we built CFPNet for real-time semantic segmentation which applied a series of
dilated convolution channels to extract effective features. Experiments on
Cityscapes and CamVid datasets show that the proposed CFPNet achieves an
effective combination of those factors. For the Cityscapes test dataset, CFPNet
achieves 70.1% class-wise mIoU with only 0.55 million parameters and 2.5 MB
memory. The inference speed can reach 30 FPS on a single RTX 2080Ti GPU with a
1024x2048-pixel image.
