GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

Anvekar, Tejas; Bazazian, Dena

doi:10.1109/cvprw59228.2023.00440

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…3 The three core strategies for supervised hyperbolic learning in computer vision. Current literature performs hyperbolic learning of visual embeddings by learning to match training samples (i) to hyperbolic class hyperplanes, i.e., gyroplanes, (ii) to hyperbolic class prototypes, or (iii) by contrasting to other samples tion (Huang et al, 2023), skeletal data (Franco et al, 2023;Chen et al, 2023), LiDAR data (Tong et al, 2022;, point clouds (Montanaro et al, 2022;Anvekar & Bazazian, 2023;Lin et al, 2023b;Onghena et al, 2023), 3D shapes (Chen et al, 2020b;Onghena et al, 2023;Leng et al, 2023), and remote sensing data (Hamzaoui et al, 2023). In summary, hyperbolic geometry has impacted a wide range of research fields.…”

Section: Non-visual Hyperbolic Learningmentioning

confidence: 99%

Hyperbolic Deep Learning in Computer Vision: A Survey

Mettes,

Ghadimi Atigh,

Keller-Ressel

et al. 2024

Int J Comput Vis

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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.

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