Document Type

Article

Publication Date

2022

Volume

26

Issue

4

Pages

1635–1647

Publisher Name

Instituto Politécnico Nacional (IPN)

Abstract

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.

Identifier

ISSN 2007-9737

Comments

Author Posting © Instituto Politécnico Nacional (IPN), 2022. This article is posted here by permission of Instituto Politécnico Nacional (IPN) for personal use. This article was published open access Computación y Sistemas, Vol. 26, ISS. 4, 2022, (March 17, 2023), http://dx.doi.org/10.13053/CyS-26-4-4436

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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