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Applications of graph convolutional networks in computer vision

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Abstract

Graph Convolutional Network (GCN) which models the potential relationship between non-Euclidean spatial data has attracted researchers’ attention in deep learning in recent years. It has been widely used in different computer vision tasks by modeling the latent space, topology, semantics, and other information in Euclidean spatial data and has achieved significant success. To better understand the work principles and future GCN applications in the computer vision field, this study reviewed the basic principles of GCN, summarized the difficulties and solutions using GCN in different visual tasks, and introduced in detail the methods for constructing graphs from the Euclidean spatial data in different visual tasks. At the same time, the review divided the application of GCN in basic visual tasks into image recognition, object detection, semantic segmentation, instance segmentation and object tracking. The role and performance of GCN in basic visual tasks were summarized and compared in detail for different tasks. This review emphasizes that the application of GCN in computer vision faces three challenges: computational complexity, the paradigm of constructing graphs from the Euclidean spatial data, and the interpretability of the model. Finally, this review proposes two future trends of GCN in the vision field, namely model lightweight and fusing GCN with other models to improve the performance of the visual model and meet the higher requirements of vision tasks.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 51674255, the Postgraduate Research and Practice Innovation Program of Jiangsu Province No.KYCX22_2564 and the Graduate Innovation Program of China University of Mining and Technology No. 2022WLKXJ114.

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  1. P. Cao, Z. Zhu, Z. Wang and Y.Zhu have contributed equally to this work.

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  1. School of Computer Science and Technology, China University of Mining and Technology, Tongshan, Xuzhou, 221006, Jiangsu, China

    Pingping Cao, Zeqi Zhu, Ziyuan Wang & Qiang Niu

  2. Department of Civil Architectural and Environmental Engineering, Missouri University of Science and Technology, 500 W. 16th Street, Rolla, MO, 65409, USA

    Yanping Zhu

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Appendix: Open-source code for real application of GCN in the field of vision

Appendix: Open-source code for real application of GCN in the field of vision

ML-GCN: http://github.com/Megvii-Nanjing/ML-GCN

ADD-GCN: http://github.com/Yejin0111/ADD-GCN

KSSNet: http://github.com/mathkey/mssnet

Sequential-GCN: http://github.com/razvancaramalau/Sequential-GCN-for-Active-Learning

S-AT GCN: http://github.com/Link2Link/FE-GCN

SIN: http://github.com/choasup/SIN

SPG: http://github.com/loicland/superpoint_graph

3DGNN: http://github.com/yanx27/3DGNN_pytorch

RGCNN: http://github.com/tegusi/RGCNN

Graph-FCN: http://github.com/muntam/FCN-graph

GMNet: http://github.com/LTTM/GMNet

BCNet: http://github.com/lkeab/BCNet

Curve-GCN:http://github.com/fidler-lab/curve-gcn

DACN: http://github.com/moliqingcha/DACN

Pixel2mesh: http://github.com/nywang16/Pixel2Mesh

SiamGAT: http://github.com/ohhhyeahhh/SiamGAT

Graph Networks for MOT: http://github.com/yinizhizhu/GNMOT

Neural-Solver: http://github.com/selflein/GraphNN-Multi-Object-Tracking

EDA-GNN: http://github.com/peizhaoli05/EDA-GNN

GCN: http://github.com/tkipf/gcn

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Cao, P., Zhu, Z., Wang, Z. et al. Applications of graph convolutional networks in computer vision. Neural Comput & Applic 34, 13387–13405 (2022). https://doi.org/10.1007/s00521-022-07368-1

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