| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560743 |
| ISSN: | 1405-5546 |
| Autores: | Dematties, Dario1 Rizzi, Silvio2 Thiruvathukal, George K.3 Wainselboim, Alejandro4 |
| Instituciones: | 1Northwestern University, Northwestern Argonne Institute of Science and Engineering, Illnois. Estados Unidos 2Argonne Leadership Computer Facility, Argonne National Laboratory, Estados Unidos 3Loyola University Chicago, Computer Science Department, Estados Unidos 4Instituto de Ciencias Humanas Sociales y Ambientales, CONICET Mendoza Technological Scientific, Argentina |
| Año: | 2022 |
| Periodo: | Oct-Dic |
| Volumen: | 26 |
| Número: | 4 |
| Paginación: | 1635-1647 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | In this paper, a series of experimental methods are presented explaining a new approach towards active foveated Computer Vision (CV). This is a collaborative effort between researchers at CONICET Mendoza Technological Scientific Center from Argentina, Argonne National Laboratory (ANL), and Loyola University Chicago from the US. The aim is to advance new CV approaches more in line with those found in biological agents in order to bring novel solutions to the main problems faced by current CV applications. Basically this work enhance Self-supervised (SS) learning, incorporating foveated vision plus saccadic behavior in order to improve training and computational efficiency without reducing performance significantly. This paper includes a compendium of methods’ explanations, and since this is a work that is currently in progress, only preliminary results are provided. We also make our code fully available.fn |
| Keyword: | Foveated computer vision, Saccadic behavior, Reinforcement learning, Self-supervised learning, General-Purpose Graphics Processing Units (GPGPUs) |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
