Towards an Active Foveated Approach to Computer Vision

Dario Dematties, Silvio Rizzi, George K. Thiruvathukal, Alejandro Wainselboim


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.


Foveated computer vision, saccadic behavior, reinforcement learning, self-supervised learning, General-Purpose Graphics Processing Units (GPGPUs)

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