A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

A distance metric known as non-Euclidean deviates from the laws of Euclidean geometry, which is geometry that governs most physical spaces. It utilized when inappropriate, for dealing with curved surfaces or spaces complex topologies. The ability to apply deep learning techniques domains including graphs, manifolds, and point clouds made possible by learning. use rapidly expanding study real-world datasets are intrinsically non-Euclidean. Over years, numerous novel have been introduced, each its benefits drawbacks. This paper provides a categorized archive approaches used in computer vision up this point. starts outlining context, pertinent information, development field’s history. Modern state-of-the-art methods described briefly application fields. also highlights model’s shortcomings tables graphs shows different applicability. Overall, work contributes collective information performance comparison will help enhance deep-learning research future.