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CyclicNet: an alternately updated network for semantic segmentation

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Abstract

In recent years, with the continuous breakthrough in deep learning, convolutional neural networks (CNNs) have shown great potential for semantic segmentation. CNNs achieve better results by deepening or widening the network, but they increase the utilization rate of computing resources and even have the phenomenon of the vanishing gradient. A new convolutional neural network architecture with alternately updated clique (CliqueNet) can get a deeper network and improves the utilization of network features. In order to maximize the transmission of semantic information, this paper introduces the clique block of CliqueNet and proposes a new fully convolutional network based on the encoder-decoder structure, which calls the CyclicNet, an alternately updated network for semantic segmentation. Besides, the long skip connections and short skip connections are added in the network to avoid the vanishing gradient. The experiment was conducted on the CamVid and Cityscapes. Comparing it with the current advanced architectures shows that CyclicNet can maximize information flow and achieve the most superior results.

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Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities (NO.2020YJ003).

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  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, China

    Guixian Wu & Yuancheng Li

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  1. Guixian Wu
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  2. Yuancheng Li
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Correspondence to Yuancheng Li.

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Wu, G., Li, Y. CyclicNet: an alternately updated network for semantic segmentation. Multimed Tools Appl 80, 3213–3227 (2021). https://doi.org/10.1007/s11042-020-09791-9

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  • DOI: https://doi.org/10.1007/s11042-020-09791-9

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