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A survey of visual neural networks: current trends, challenges and opportunities

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Abstract

Research of visual neural networks (VNNs) is one of the most important topics in deep learning and has received wide attention from industry and academia for their promising performance. The applications of VNNs range from image classification and target detection to scene segmentation in various fields such as transportation, healthcare and finance. In general, VNNs can be divided into two types: Convolutional neural networks (CNNs) and Transformer networks. In the last decade, CNNs have dominated the research of vision tasks. Recently, Transformer networks are successfully used in the fields of natural language processing and computer vision, and have achieved remarkable performance in many vision tasks. In this paper, the basic architectures and current trends of these two types of VNNs are first introduced. Then, three major challenges of VNNs are pointed out: scalability, robustness and interpretability. Next, the lightweight, robust and interpretable solutions are summarized and analyzed. Finally, the future opportunities of VNNs are presented.

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Acknowledgements

This work is partially supported by the Guangxi Natural Science Foundation (2022GXNSFAA035506), the National Natural Science Foundation of China (62272111, 61962008), Guangxi “Bagui Scholar” Team for Innovation and Research, Guangxi Talent Highland Project of Big Data Intelligence and Application, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, and the Innovation Project of Guangxi Graduate Education (YCBZ2022063, YCSW2022177). Many thanks to the reviewers for their helpful suggestions.

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  1. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, People’s Republic of China

    Ping Feng & Zhenjun Tang

  2. School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, People’s Republic of China

    Zhenjun Tang

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PF did the main work and wrote the draft manuscript. ZT supervised the work and wrote the manuscript. All authors read and approved the final manuscript.

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Feng, P., Tang, Z. A survey of visual neural networks: current trends, challenges and opportunities. Multimedia Systems 29, 693–724 (2023). https://doi.org/10.1007/s00530-022-01003-8

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