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Towards a New Paradigm for Brain-inspired Computer Vision
Xiao-Long Zou1,2; Tie-Jun Huang1,3,4; Si Wu1,2,4
Source PublicationMachine Intelligence Research
ISSN2731-538X
2022
Volume19Issue:5Pages:412-424
Abstract

Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques. However, its progress so far is not impressing. We recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision, i.e., the biological vision is targeted for processing spatio-temporal patterns. Recently, a new paradigm for developing brain-inspired computer vision is emerging, which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of data. In this paper, we review some recent primary works towards this new paradigm, including the development of spike cameras which acquire spiking signals directly from visual scenes, and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns, including models for object detection, tracking, and recognition. We also discuss about the future directions to improve the paradigm.

KeywordBrain-inspired computer vision spatio-temporal patterns object detection object tracking object recognition
DOI10.1007/s11633-022-1370-z
Sub direction classification其他
planning direction of the national heavy laboratory其他
Paper associated data
Chinese guidehttps://mp.weixin.qq.com/s/ndomFyl5DAZj8xYKQoosQQ
Video parsinghttps://www.bilibili.com/video/BV1pe4y1G7x8/
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55953
Collection学术期刊_Machine Intelligence Research
Affiliation1.Beijing Academy of Artificial Intelligence, Beijing 100084, China
2.School of Psychology and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Center for Quantitative Biology, PKU-Tsinghua Center for Life Sciences, Peking University, Beijing 100084, China
3.National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing 100871, China
4.Institute for Artificial Intelligence, Peking University, Beijing 100871, China
Recommended Citation
GB/T 7714
Xiao-Long Zou,Tie-Jun Huang,Si Wu. Towards a New Paradigm for Brain-inspired Computer Vision[J]. Machine Intelligence Research,2022,19(5):412-424.
APA Xiao-Long Zou,Tie-Jun Huang,&Si Wu.(2022).Towards a New Paradigm for Brain-inspired Computer Vision.Machine Intelligence Research,19(5),412-424.
MLA Xiao-Long Zou,et al."Towards a New Paradigm for Brain-inspired Computer Vision".Machine Intelligence Research 19.5(2022):412-424.
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