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Towards a New Paradigm for Brain-inspired Computer Vision

Machine Intelligence Research volume 19, pages 412–424 (2022)Cite this article

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.

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Acknowledgements

This work was supported by National Key R&D Program of China (No. 2020AAA0105200), Science and Technology Innovation 2030-Brain Science and Brain-inspired Intelligence Project (No. 2021ZD0200204), National Key Research and Development Program of China (No. 2020AAA0130401), Huawei Technology Co., Ltd, China (No. YBN2019105137).

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Authors and Affiliations

  1. Beijing Academy of Artificial Intelligence, Beijing, 100084, China

    Xiao-Long Zou, Tie-Jun Huang & Si Wu

  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

    Xiao-Long Zou & Si Wu

  3. National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, 100871, China

    Tie-Jun Huang

  4. Institute for Artificial Intelligence, Peking University, Beijing, 100871, China

    Tie-Jun Huang & Si Wu

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  1. Xiao-Long Zou
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Correspondence to Si Wu.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Xiao-Long Zou received B. Sc. degree in vehicle engineering from Jilin University, China in 2012, and the Ph. D. degree in system theory from Beijing Normal University, China in 2018. Currently, he is a postdoctoral fellow in Beijing Academy of Artificial Intelligence and School of Psychological and Cognitive Sciences, Peking University, China.

His research interests include computational neuroscience and brain-inspired computing.

Tie-Jun Huang (Senior Member, IEEE) received the Ph. D. degree in pattern recognition and intelligent system from the Huazhong (Central China) University of Science and Technology, China in 1998. He is currently a professor in School of Computer Science, Peking University, China, and the director of Beijing Academy for Artificial Intelligence. He has published two books, more than 200 peer-reviewed papers on leading journals and conferences, held more than 50 granted patents, and is the co-editor of four ISO/IEC standards, five national standards of China, and four IEEE standards.

He is a fellow of CAAI and CCF, the Secretary General of the Artificial Intelligence Industry Technology Innovation Alliance, and the vice chair of the China National General Group on AI Standardization. He received the National Award for Science and Technology of China (Tier-2) for three times. He was awarded the Distinguished Young Scholar by the National Natural Science Foundation of China in 2014 and the Distinguished Professor of the Chang Jiang Scholars Program by the Ministry of Education of China in 2015.

His research interests include visual information processing and neuromorphic computing.

Si Wu received the B. Sc. degree in general physics in 1990, the M. Sc degree in general relativity in 1992, and the Ph. D. degree in statistical physics in 1995, all from Beijing Normal University, China. Currently, he is a professor in School of Psychological and Cognitive Sciences, PI in PKU-IDG/McGovern Institute for Brain Research, PI in Center for Quantitative Biology, and PI in PKU-Tsinghua Center for Life Sciences, Peking University, China. He is also a researcher in Beijing Academy of Artificial Intelligence, China, and Co-Editor-in-Chief of Frontiers in Computational Neuroscience.

His research interests include computational neuroscience and brain-inspired computing.

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Zou, XL., Huang, TJ. & Wu, S. Towards a New Paradigm for Brain-inspired Computer Vision. Mach. Intell. Res. 19, 412–424 (2022). https://doi.org/10.1007/s11633-022-1370-z

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Keywords

  • Brain-inspired computer vision
  • spatio-temporal patterns
  • object detection
  • object tracking
  • object recognition