Self-Supervised Vision Graph Neural Networks Based on Contrastive Learning

In the field of computer vision, Vision Graph Neural Networks (ViG) have demonstrated significant potential in image understanding. By treating the divided image patches as nodes and constructing connection relationships based on neighbor attributes, ViG can efficiently model global dependencies within images with the help of graph attributes. However, most existing ViG methods have the problem of high computational complexity in graph construction and may not be able to effectively and fully explore the graph structure information. Besides, the heavy reliance on manual annotation labels limits the application potential of ViG in practical scenarios. To this end, in this paper, we propose a novel self-supervised vision graph contrastive learning method (S2ViG) based on image mixing strategy for efficient vision graph representation learning. It aims to use self-supervised method to alleviate the dependence on manual annotation and enhance the understanding of the global structure of the graph using two different vision graph construction methods. Specifically, we first employ image mixing strategy to uncover latent semantic relationships among multiple images. Then, we construct dynamic graph structures for image patches from local and global perspectives to obtain augmented contrastive samples. Finally, the multilevel contrastive loss is constructed to optimize the network. Experimental results show that our method achieves excellent performance on multiple datasets such as ImageNet-1K and CIFAR.