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 ...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.展开更多
Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans becom...Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.Here,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.展开更多
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 num...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.展开更多
基金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)+1 种基金National Key Research and Development Program of China(No.2020AAA0130401)Huawei Technology Co.,Ltd,China(No.YBN2019105137)。
文摘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.
基金supported by National Natural Science Foundation of China(Nos.62176003 and 62088102)the Royal Society Newton Advanced Fellowship of the UK(No.NAF-R1-191082)。
文摘Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.Here,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.
基金This work was supported in part by the National Key R&D Program of China(2018YFB1004600)the National Natural Science Foundation of China(Grant Nos.61761146004,61773375)+1 种基金the Beijing Municipal Natural Science Foundation(Z181100008918010)Chinese Academy of Sciences(153D31KYSB20160282).
文摘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.
