Review of Image Classification Algorithms Based on Graph Convolutional Networks

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 the graph structure, such as social networks, recommender systems, and molecular structures. This paper summarizes the classical convolutional neural network models, highlighting their innovative approaches. And it will introduce the problems that graph convolutional networks have had, such as over-smoothing, and the ways to solve them, and suggest some possible future directions.