Recent Research Progress of Graph Neural Networks in Computer Vision

Graph neural networks (GNNs) have demonstrated significant potential in the field of computer vision in recent years, particularly in handling non-Euclidean data and capturing complex spatial and semantic relationships. This paper provides a comprehensive review of the latest research on GNNs in computer vision, with a focus on their applications in image processing, video analysis, and multimodal data fusion. First, we briefly introduce common GNN models, such as graph convolutional networks (GCN) and graph attention networks (GAT), and analyze their advantages in image and video data processing. Subsequently, this paper delves into the applications of GNNs in tasks such as object detection, image segmentation, and video action recognition, particularly in capturing inter-region dependencies and spatiotemporal dynamics. Finally, the paper discusses the applications of GNNs in multimodal data fusion tasks such as image–text matching and cross-modal retrieval, and highlights the main challenges faced by GNNs in computer vision, including computational complexity, dynamic graph modeling, heterogeneous graph processing, and interpretability issues. This paper provides a comprehensive understanding of the applications of GNNs in computer vision for both academia and industry and envisions future research directions.