Locally-Connected, Irregular Deep Neural Networks For Biomimetic Active Vision In A Simulated Human

2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)(2020)

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摘要
An advanced simulation framework has recently been introduced for exploring human perception and visuomotor control. In this context, we investigate locally-connected, irregular deep neural networks (liNets) for biomimetic active vision. Like commonly used CNNs, liNets are locally-connected, forming receptive fields, but unlike CNNs, they are suitable for spatially irregular photoreceptor distributions inspired by those found in foveated biological retinas. Compared to fully-connected deep neural networks, liNets accommodate a much greater number of retinal photoreceptors to enhance visual acuity without intractable memory consumption. LiNets serve well in the biomimetic active vision system embodied in a simulated human that learns active visuomotor control and active appearance-based recognition.
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关键词
active visuomotor control,active appearance-based recognition,irregular deep neural networks,simulated human,advanced simulation framework,human perception,liNets,CNNs,receptive fields,spatially irregular photoreceptor distributions,foveated biological retinas,LiNets,biomimetic active vision system
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