Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision

While abundant in biology, foveated vision is nearly absent from computational models and especially deep learning architectures. Despite considerable hardware improvements, training deep neural networks still presents a challenge and constraints complexity of models. Here we propose an end-to-end n...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hristofor Lukanov, Peter König, Gordon Pipa
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	space-variant vision active vision foveal vision peripheral vision deep learning-artificial neural network (DL-ANN) bottom-up attention Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/dd5d9cfd27fd412e8e6bb0d0b7609983
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:dd5d9cfd27fd412e8e6bb0d0b7609983
record_format	dspace
spelling	oai:doaj.org-article:dd5d9cfd27fd412e8e6bb0d0b76099832021-11-22T05:13:39ZBiologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision1662-518810.3389/fncom.2021.746204https://doaj.org/article/dd5d9cfd27fd412e8e6bb0d0b76099832021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fncom.2021.746204/fullhttps://doaj.org/toc/1662-5188While abundant in biology, foveated vision is nearly absent from computational models and especially deep learning architectures. Despite considerable hardware improvements, training deep neural networks still presents a challenge and constraints complexity of models. Here we propose an end-to-end neural model for foveal-peripheral vision, inspired by retino-cortical mapping in primates and humans. Our model has an efficient sampling technique for compressing the visual signal such that a small portion of the scene is perceived in high resolution while a large field of view is maintained in low resolution. An attention mechanism for performing “eye-movements” assists the agent in collecting detailed information incrementally from the observed scene. Our model achieves comparable results to a similar neural architecture trained on full-resolution data for image classification and outperforms it at video classification tasks. At the same time, because of the smaller size of its input, it can reduce computational effort tenfold and uses several times less memory. Moreover, we present an easy to implement bottom-up and top-down attention mechanism which relies on task-relevant features and is therefore a convenient byproduct of the main architecture. Apart from its computational efficiency, the presented work provides means for exploring active vision for agent training in simulated environments and anthropomorphic robotics.Hristofor LukanovPeter KönigPeter KönigGordon PipaFrontiers Media S.A.articlespace-variant visionactive visionfoveal visionperipheral visiondeep learning-artificial neural network (DL-ANN)bottom-up attentionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Computational Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	space-variant vision active vision foveal vision peripheral vision deep learning-artificial neural network (DL-ANN) bottom-up attention Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	space-variant vision active vision foveal vision peripheral vision deep learning-artificial neural network (DL-ANN) bottom-up attention Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Hristofor Lukanov Peter König Peter König Gordon Pipa Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision
description	While abundant in biology, foveated vision is nearly absent from computational models and especially deep learning architectures. Despite considerable hardware improvements, training deep neural networks still presents a challenge and constraints complexity of models. Here we propose an end-to-end neural model for foveal-peripheral vision, inspired by retino-cortical mapping in primates and humans. Our model has an efficient sampling technique for compressing the visual signal such that a small portion of the scene is perceived in high resolution while a large field of view is maintained in low resolution. An attention mechanism for performing “eye-movements” assists the agent in collecting detailed information incrementally from the observed scene. Our model achieves comparable results to a similar neural architecture trained on full-resolution data for image classification and outperforms it at video classification tasks. At the same time, because of the smaller size of its input, it can reduce computational effort tenfold and uses several times less memory. Moreover, we present an easy to implement bottom-up and top-down attention mechanism which relies on task-relevant features and is therefore a convenient byproduct of the main architecture. Apart from its computational efficiency, the presented work provides means for exploring active vision for agent training in simulated environments and anthropomorphic robotics.
format	article
author	Hristofor Lukanov Peter König Peter König Gordon Pipa
author_facet	Hristofor Lukanov Peter König Peter König Gordon Pipa
author_sort	Hristofor Lukanov
title	Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision
title_short	Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision
title_full	Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision
title_fullStr	Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision
title_full_unstemmed	Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision
title_sort	biologically inspired deep learning model for efficient foveal-peripheral vision
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/dd5d9cfd27fd412e8e6bb0d0b7609983
work_keys_str_mv	AT hristoforlukanov biologicallyinspireddeeplearningmodelforefficientfovealperipheralvision AT peterkonig biologicallyinspireddeeplearningmodelforefficientfovealperipheralvision AT peterkonig biologicallyinspireddeeplearningmodelforefficientfovealperipheralvision AT gordonpipa biologicallyinspireddeeplearningmodelforefficientfovealperipheralvision
_version_	1718418192391471104

Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision

Ejemplares similares