I'm currently working on a project where I want to utilize Graph Neural Networks (GNNs) for image classification tasks. However, I'm facing difficulties in understanding how to implement GNNs specifically for classification purposes.
I have explored convolutional neural networks (CNNs) for image classification, but I believe GNNs could provide valuable insights by capturing the relationships between pixels as a graph structure. I would greatly appreciate it if someone could guide me on how to effectively use GNNs for image classification.
Specifically, I would like to gain insights into the following aspects:
- How can I represent images as graphs for GNN-based classification? What should be the nodes, edges, and features in this context?
- What are the recommended GNN architectures or models that have been successfully applied to image classification tasks?
- Are there any code examples, tutorials, or libraries available that can assist me in implementing GNNs for image classification?
I have a decent understanding of deep learning and have worked with CNNs before, but I'm new to GNNs and their application to image classification. Any guidance, code snippets, or reference materials that could help me get started with GNN-based image classification would be highly appreciated. Thank you!