Modern computer vision approaches mainly relied on convolutions neural networks, which view the images as regular grid structures. More recently, different approaches have been proposed to overcome the limitations, such as the lack of flexibility, and enhance the receptive fields of neural network architectures. To address these limitations, graph-based neural networks have garnered increasing interest for computer vision tasks. Instead of a grid, these methods represent images as graphs that encapsulate relationships between spatial regions. In the graph, nodes correspond to image patches or regions, while edges characterize the spatial and semantic connections between them. Consequently, this representation provides a more adaptable way of encoding both local and long-range dependencies within the visual scene.