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Recurrent Feedback Improves Recognition of Partially Occluded Objects

2021-04-21 16:18:34
Markus Roland Ernst, Jochen Triesch, Thomas Burwick
arXiv_CV
       
Abstract

Recurrent connectivity in the visual cortex is believed to aid object recognition for challenging conditions such as occlusion. Here we investigate if and how artificial neural networks also benefit from recurrence. We compare architectures composed of bottom-up, lateral and top-down connections and evaluate their performance using two novel stereoscopic occluded object datasets. We find that classification accuracy is significantly higher for recurrent models when compared to feedforward models of matched parametric complexity. Additionally we show that for challenging stimuli, the recurrent feedback is able to correctly revise the initial feedforward guess.

Abstract (translated)

URL

https://arxiv.org/abs/2104.10615

PDF

https://arxiv.org/pdf/2104.10615.pdf

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