Biologically inspired visual computing: the state of the art

doi:10.1007/s11704-020-9001-8

Front. Comput. Sci.

2021, Vol. 15

Issue (1) : 151304 https://doi.org/10.1007/s11704-020-9001-8

REVIEW ARTICLE

Biologically inspired visual computing: the state of the art

Wangli HAO^1,⁴, Ian Max ANDOLINA⁶, Wei WANG^3,^4,^5,⁶, Zhaoxiang ZHANG^1,^2,^3,⁴(

)

¹. Research Center for Research on Intelligent Perception and Computing, Beijing 100190, China
². National Laboratory of Pattern Recognition, CASIA, Beijing 100190, China
³. CAS Center for Excellence in Brain Science and Intelligence Technology, CAS, Beijing 100190, China
⁴. University of Chinese Academy of Sciences, Beijing 100190, China
⁵. State Key Laboratory of Neuroscience, Shanghai 200031, China
⁶. Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China

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Abstract

Visual information is highly advantageous for the evolutionary success of almost all animals. This information is likewise critical for many computing tasks, and visual computing has achieved tremendous successes in numerous applications over the last 60 years or so. In that time, the development of visual computing has moved forwards with inspiration from biological mechanisms many times. In particular, deep neural networks were inspired by the hierarchical processing mechanisms that exist in the visual cortex of primate brains (including ours), and have achieved huge breakthroughs in many domainspecific visual tasks. In order to better understand biologically inspired visual computing, we will present a survey of the current work, and hope to offer some new avenues for rethinking visual computing and designing novel neural network architectures.

Keywords brain-inspired vision neural models intelligence novel neural networks

Corresponding Author(s): Wangli HAO,Zhaoxiang ZHANG

Just Accepted Date: 26 February 2020 Issue Date: 24 September 2020

Cite this article:

Wangli HAO,Ian Max ANDOLINA,Wei WANG, et al. Biologically inspired visual computing: the state of the art[J]. Front. Comput. Sci., 2021, 15(1): 151304.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-9001-8
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I1/151304

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