  Graph Neural Networks (GNNs) have gained momentum in graph representation
learning and boosted the state of the art in a variety of areas, such as data
mining (\emph{e.g.,} social network analysis and recommender systems), computer
vision (\emph{e.g.,} object detection and point cloud learning), and natural
language processing (\emph{e.g.,} relation extraction and sequence learning),
to name a few. With the emergence of Transformers in natural language
processing and computer vision, graph Transformers embed a graph structure into
the Transformer architecture to overcome the limitations of local neighborhood
aggregation while avoiding strict structural inductive biases. In this paper,
we present a comprehensive review of GNNs and graph Transformers in computer
vision from a task-oriented perspective. Specifically, we divide their
applications in computer vision into five categories according to the modality
of input data, \emph{i.e.,} 2D natural images, videos, 3D data, vision +
language, and medical images. In each category, we further divide the
applications according to a set of vision tasks. Such a task-oriented taxonomy
allows us to examine how each task is tackled by different GNN-based approaches
and how well these approaches perform. Based on the necessary preliminaries, we
provide the definitions and challenges of the tasks, in-depth coverage of the
representative approaches, as well as discussions regarding insights,
limitations, and future directions.
