Hyperbolic Deep Learning in Computer Vision: A Survey
It provides a comprehensive overview for researchers in computer vision interested in hyperbolic geometry, but is incremental as it synthesizes existing work without novel contributions.
This survey paper categorizes and reviews current literature on hyperbolic deep learning in computer vision, highlighting its potential for embedding hierarchical structures, learning from limited samples, and improving robustness, but does not present new experimental results or concrete numbers.
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.