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2019 | OriginalPaper | Buchkapitel

Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders

verfasst von : Markus Roland Ernst, Jochen Triesch, Thomas Burwick

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

Verlag: Springer International Publishing

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Abstract

Recurrent connections in the visual cortex are thought to aid object recognition when part of the stimulus is occluded. Here we investigate if and how recurrent connections in artificial neural networks similarly aid object recognition. We systematically test and compare architectures comprised of bottom-up (B), lateral (L) and top-down (T) connections. Performance is evaluated on a novel stereoscopic occluded object recognition dataset. The task consists of recognizing one target digit occluded by multiple occluder digits in a pseudo-3D environment. We find that recurrent models perform significantly better than their feedforward counterparts, which were matched in parametric complexity. Furthermore, we analyze how the network’s representation of the stimuli evolves over time due to recurrent connections. We show that the recurrent connections tend to move the network’s representation of an occluded digit towards its un-occluded version. Our results suggest that both the brain and artificial neural networks can exploit recurrent connectivity to aid occluded object recognition.

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Titel: Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders
verfasst von: Markus Roland Ernst
Jochen Triesch
Thomas Burwick
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing
Print ISBN: 978-3-030-30507-9

Electronic ISBN: 978-3-030-30508-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-30508-6_24

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