Byung‐Gyu Kim

Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification. The authors explore in depth the applications of GNNs in computer vision, including their design considerations, architectural challenges, applications, and implementation concerns. While conventional convolutional neural networks (CNNs) excel at object recognition in images and videos, GNN architectures offer a novel method for addressing various image and video comprehension challenges. A novel deep neural network‐based model for image and video analysis is proposed, which combines a neural network with fully connected layers on a graph. The proposed architecture extracts highly discriminative information from images and videos by leveraging the graph structure. Also, the investigation focuses on the enhancement of underlying connection network estimation using cutting‐edge graph learning algorithms. Experimental results on real‐world datasets demonstrate that the proposed GNN model is preferable to existing state‐of‐the‐art methods. It obtains a remarkable 96.63% accuracy on the ImageNet dataset, outperforming heuristic approaches, artificial neural networks, and conventional CNN techniques. From the results, we can see that GNNs are a potent instrument for graph data analysis and pave the way for machines to achieve human‐level visual intuition.