Wu Tianyi, Tang Sheng, Zhang Rui, Cao Juan, Zhang Yongdong
IEEE Trans Image Process. 2021;30:1169-1179. doi: 10.1109/TIP.2020.3042065. Epub 2020 Dec 17.
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.
在移动设备上应用语义分割模型的需求一直在迅速增长。当前最先进的网络有大量参数，因此不适用于移动设备，而其他内存占用小的模型遵循分类网络的思路，忽略了语义分割的固有特性。为了解决这个问题，我们提出了一种新颖的上下文引导网络（CGNet），它是一种用于语义分割的轻量级高效网络。我们首先提出了上下文引导（CG）块，它能有效且高效地学习局部特征和周围上下文的联合特征，并利用全局上下文进一步改进联合特征。基于CG块，我们开发了在网络所有阶段都能捕捉上下文信息的CGNet。CGNet是专门为利用语义分割的固有特性并提高分割精度而设计的。此外，CGNet经过精心设计以减少参数数量并节省内存占用。在参数数量相等的情况下，所提出的CGNet显著优于现有的轻量级分割网络。在Cityscapes和CamVid数据集上的大量实验验证了所提方法的有效性。具体而言，在没有任何后处理和多尺度测试的情况下，所提出的CGNet在Cityscapes上以少于0.5M的参数实现了64.8%的平均交并比。
