  Deep representation learning is a ubiquitous part of modern computer vision.
While Euclidean space has been the de facto standard manifold for learning
visual representations, hyperbolic space has recently gained rapid traction for
learning in computer vision. Specifically, hyperbolic learning has shown a
strong potential to embed hierarchical structures, learn from limited samples,
quantify uncertainty, add robustness, limit error severity, and more. In this
paper, we provide a categorization and in-depth overview of current literature
on hyperbolic learning for computer vision. We research both supervised and
unsupervised literature and identify three main research themes in each
direction. We outline how hyperbolic learning is performed in all themes and
discuss the main research problems that benefit from current advances in
hyperbolic learning for computer vision. Moreover, we provide a high-level
intuition behind hyperbolic geometry and outline open research questions to
further advance research in this direction.
