Object detection and segmentation using GNNs

Object detection and segmentation are crucial tasks in CV, with applications ranging from autonomous driving to medical image analysis. While CNNs have been the go-to approach for these tasks, GNNs are emerging as a powerful alternative or complementary technique. This section will explore how GNNs can be applied to object detection and segmentation tasks while discussing various approaches and their advantages.

Graph-based object proposal generation

Object proposal generation is often the first step in many object detection pipelines. Traditional methods rely on sliding windows or region proposal networks, but graph-based approaches offer an interesting alternative. By representing an image as a graph, we can leverage the relational inductive bias of GNNs to generate more informed object proposals.

For example, consider an image represented as a graph of superpixels. Each superpixel (node) might have features such as color histograms...