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

Author:

Chowdhury Sakib H.1ORCID,Sany Md. Robius1ORCID,Ahamed Md. Hafiz1ORCID,Das Sajal K.1ORCID,Badal Faisal Rahman1ORCID,Das Prangon1ORCID,Tasneem Zinat1ORCID,Hasan Md. Mehedi1ORCID,Islam Md. Robiul1ORCID,Ali Md. Firoj1ORCID,Abhi Sarafat Hussain1ORCID,Islam Md. Manirul1ORCID,Sarker Subrata Kumar1ORCID

Affiliation:

1. Department of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

Abstract

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

Publisher

Hindawi Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

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