  2D image understanding is a complex problem within computer vision, but it
holds the key to providing human-level scene comprehension. It goes further
than identifying the objects in an image, and instead, it attempts to
understand the scene. Solutions to this problem form the underpinning of a
range of tasks, including image captioning, visual question answering (VQA),
and image retrieval. Graphs provide a natural way to represent the relational
arrangement between objects in an image, and thus, in recent years graph neural
networks (GNNs) have become a standard component of many 2D image understanding
pipelines, becoming a core architectural component, especially in the VQA group
of tasks. In this survey, we review this rapidly evolving field and we provide
a taxonomy of graph types used in 2D image understanding approaches, a
comprehensive list of the GNN models used in this domain, and a roadmap of
future potential developments. To the best of our knowledge, this is the first
comprehensive survey that covers image captioning, visual question answering,
and image retrieval techniques that focus on using GNNs as the main part of
their architecture.
