  The demand of applying semantic segmentation model on mobile devices has been
increasing rapidly. Current state-of-the-art networks have enormous amount of
parameters hence unsuitable for mobile devices, while other small memory
footprint models follow the spirit of classification network and ignore the
inherent characteristic of semantic segmentation. To tackle this problem, we
propose a novel Context Guided Network (CGNet), which is a light-weight and
efficient network for semantic segmentation. We first propose the Context
Guided (CG) block, which learns the joint feature of both local feature and
surrounding context, and further improves the joint feature with the global
context. Based on the CG block, we develop CGNet which captures contextual
information in all stages of the network and is specially tailored for
increasing segmentation accuracy. CGNet is also elaborately designed to reduce
the number of parameters and save memory footprint. Under an equivalent number
of parameters, the proposed CGNet significantly outperforms existing
segmentation networks. Extensive experiments on Cityscapes and CamVid datasets
verify the effectiveness of the proposed approach. Specifically, without any
post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean
IoU on Cityscapes with less than 0.5 M parameters. The source code for the
complete system can be found at https://github.com/wutianyiRosun/CGNet.
