“…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.…”