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

By Tianyi Wu, Sheng Tang, Rui Zhang, Yongdong Zhang. IEEE Transactions on Image Processing, 2018

786 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bbd8411033f125b06ad0b59912cfb49395b3cc82

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Citations in the corpus

Pixel Difference Networks for Efficient Edge Detection (2021, background): “Alternatively, lightweight architectures for other dense prediction tasks [13, 58, 43, 25, 38, 63] and multi-task learning [24, 26] may also benefit edge detection.”
Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation (2019, methodology): “To show the advantages of LEDNet, we selected 6 state-of-the-art lightweight networks as baselines, including SegNet [7], ENet [13], ERFNet [14], ICNet [19], CGNet [25], and ESPNet [20].”
Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild (2021, methodology): “For this reason, we train 4 accurate yet computationally-expensive networks including SegNet [10], PSPNet [14], DenseASPP [3] and DANet [26], as well as 4 efﬁcient networks including CGNet [38], SwiftNet [39], SwaftNet [4] and ERF-PSPNet [1], as shown in Table V.”
Few-shot Video-to-Video Synthesis (2019, methodology): “We apply a pretrained segmentation network [60] to get the segmentation maps.”
A Review on Deep Learning in UAV Remote Sensing (2021, neutral): “Still, as of today, the CGnet [35] and DLNet [47] are considered the state-of-art methods for semantic segmentation.”
Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, neutral): “And FastICENet is close to CGNet [31] , LinkNet [32] and PIDNet-S [20] in accuracy, but our segmentation speed is far ahead of these networks.”
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers (2022, methodology): “We compare our models against RGB-only methods covering CNN-based [100], [117], [118], [120] and transformerbased [24], [46], [119] methods.”