CGNet: A Light-weight Context Guided Network for Semantic Segmentation

• Published in: IEEE transactions on image processing Vol. 30; p. 1
• Main Authors: Wu, Tianyi, Tang, Sheng, Rui, Zhang, Cao, Juan, Zhang, Yongdong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Computer architecture; Context; Context Guided; Context modeling; Electronic devices; Global Context; Image segmentation; Mathematical models; Memory devices; Mobile handsets; Parameters; Post-production processing; Predictive models; Semantic Segmentation; Semantics; Surrounding Context
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2020.3042065

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 effectively and efficiently, 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. CGNet is specially tailored to exploit the inherent property of semantic segmentation and increase the segmentation accuracy. Moreover, CGNet is elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing light-weight 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.