Research Article
Review of Image Classification Algorithms Based on Graph Convolutional Networks
@ARTICLE{10.4108/airo.3462, author={Wenhao Tang}, title={Review of Image Classification Algorithms Based on Graph Convolutional Networks}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={2}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2023}, month={7}, keywords={Graph Convolutional Networks, Convolutional Neural Networks, Graph Neural Networks, Over-smoothing}, doi={10.4108/airo.3462} }- Wenhao Tang
Year: 2023
Review of Image Classification Algorithms Based on Graph Convolutional Networks
AIRO
EAI
DOI: 10.4108/airo.3462
Abstract
In recent years, graph convolutional networks (GCNs) have gained widespread attention and applications in image classification tasks. While traditional convolutional neural networks (CNNs) usually represent images as a two-dimensional grid of pixels when processing image data, the classical model of graph neural networks (GNNs), GCNs, can effectively handle data with graph structure, such as social networks, recommender systems, and molecular structures. In this paper, we will introduce the problems that graph convolutional networks have had, such as over-smoothing, and the methods to solve them, and suggest some possible future directions.
Copyright © 2023 Tang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
