Image to Graph

Image-to-graph methods aim to represent image data as graphs, capturing both object identities and their relationships for improved downstream tasks like scene understanding and medical image analysis. Current research emphasizes developing efficient and robust image-to-graph transformers and graph convolutional networks (GCNs), often incorporating techniques like cross-domain learning and multi-head architectures to handle diverse data and improve accuracy. These advancements are impacting various fields, enabling more sophisticated image analysis in applications ranging from medical image segmentation and molecular structure recognition to scene graph generation and object-centric representation learning. The ability to leverage graph structures enhances the representation of complex spatial relationships within images, leading to more accurate and informative analyses.