GNNs for image classification
Image classification, a fundamental task in CV, has traditionally been dominated by CNNs. However, GNNs are emerging as a powerful alternative, offering unique advantages in capturing global structure and long-range dependencies. This section will explore how GNNs can be applied to image classification tasks while discussing various architectures and techniques.
Graph convolutional networks for image data
Graph convolutional networks (GCNs) form the backbone of many graph-based approaches to image classification. Unlike traditional CNNs that operate on regular grid-like structures, GCNs can work with irregular graph structures, making them more flexible in representing image data.
To apply GCNs to images, we need to convert the image into a graph structure. This can be done using any of the methods discussed in the previous section, such as pixel-level graphs or superpixel graphs. Once we have the graph representation, we can apply graph convolutions...
