基本信息
孔庆群  女  硕导  中国科学院自动化研究所
电子邮件: qingqun.kong@ia.ac.cn
通信地址: 北京市海淀区中关村东路95号
邮政编码: 100190

研究领域

本人致力于类脑视觉方向的研究,包括:

1. 基于第三代神经网络--脉冲神经网络的增量学习研究

2. 基于视网膜的视觉信号编码研究

3. 视觉系统的组织原理研究

招生信息

   
招生专业
081104-模式识别与智能系统
招生方向
类脑视觉

教育背景

2008-09--2013-07   中国科学院自动化研究所   工学博士学位

工作经历

   
工作简历
2015-10~现在, 中国科学院自动化研究所, 副研究员
2013-07~2015-10,中国科学院自动化研究所, 助理研究员
社会兼职
2016-11-30-今,中国人工智能学会模式识别专委会, 专委委员
2016-11-30-今,中国自动化学会模式识别与机器智能专委会, 专委委员
2014-10-10-今,中国计算机学会, 会员

教授课程

2017-2018学年类脑智能导论
2017-2017学年类脑智能概论

专利与奖励

   
专利成果
( 1 ) 基于视觉恐惧反应脑机制的应急避障方法, 发明, 2017, 第 2 作者, 专利号: 201611059266.9
( 2 ) 运动检测方法及躲避和跟踪目标的方法, 发明, 2017, 第 4 作者, 专利号: 201611046531.X
( 3 ) 类脑多模态融合方法及装置, 发明, 2020, 第 1 作者, 专利号: 201711296149.9

出版信息

   
发表论文
[1] Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang, Yuwei Wang, Yang Li, Xin Liu, Chengcheng Du, Qingqun Kong, Zizhe Ruan, Weida Bi. BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation. PATTERNS[J]. 2023, 4(8): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435966/.
[2] Fan, Bin, Kong, Qingqun, Zhang, Baoqian, Liu, Hongmin, Pan, Chunhong, Lu, Jiwen. Efficient nearest neighbor search in high dimensional hamming space. PATTERN RECOGNITION[J]. 2020, 99: http://dx.doi.org/10.1016/j.patcog.2019.107082.
[3] Zhao, Feifei, Kong, Qingqun, Zeng, Yi, Xu, Bo. A Brain-Inspired Visual Fear Responses Model for UAV Emergent Obstacle Dodging. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS[J]. 2020, 12(1): 124-132, http://dx.doi.org/10.1109/TCDS.2019.2939024.
[4] Fan, Bin, Kong, Qingqun, Wang, Xinchao, Wang, Zhiheng, Xiang, Shiming, Pan, Chunhong, Fua, Pascal. A Performance Evaluation of Local Features for Image-Based 3D Reconstruction. IEEE TRANSACTIONS ON IMAGE PROCESSING[J]. 2019, 28(10): 4774-4789, https://www.webofscience.com/wos/woscc/full-record/WOS:000480312800005.
[5] Kong, Qingqun, Han, Jiuqi, Zeng, Yi, Xu, Bo. Efficient coding matters in the organization of the early visual system. NEURAL NETWORKS[J]. 2018, 105: 218-226, http://dx.doi.org/10.1016/j.neunet.2018.04.019.
[6] Luo, Hengliang, Kong, Qingqun, Wu, Fuchao, IEEE. Traffic Sign Image Synthesis with Generative Adversarial Networks. 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)null. 2018, 2540-2545, [7] Kang, Xiaomei, Kong, Qingqun, Zeng, Yi, Xu, Bo. A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE[J]. 2018, 12(12): https://doaj.org/article/375b0c7e0e1741e8a2aa81fe0d27db41.
[8] Zhiheng Wang, Bin Fan, Qingqun Kong, Wei Sui, Xinchao Wang, Shiming Xiang, Chunhong Pan, Pascal Fua. Do We Need Binary Features for 3D Reconstruction?. 2016, http://arxiv.org/abs/1602.04502.
[9] Fan, Bin, Kong, Qingqun, Sui, Wei, Wang, Zhiheng, Wang, Xinchao, Xiang, Shiming, Pan, Chunhong, Fua, Pascal, IEEE. Do We Need Binary Features for 3D Reconstruction?. PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016)null. 2016, 1126-1135, [10] Kong Qingqun, Zeng Yi, Dong Qiulei, IEEE. BIOLOGICALLY INSPIRED DEEP STEREO MODEL. 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)null. 2015, 3700-3704, [11] Han Jiuqi, Kong Qingqun, Zeng Yi, Hao Hongwei, IEEE. HEVS: A Hierarchical Computational Model for Early Stages of the Visual System. 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)null. 2015, [12] Jiuqi Han, Qingqun Kong, Yi Zeng, Hongwei Hao. HEVS: A Hierarchical Computational Model for Early Stages of the Visual System. PROCEEDINGSOFTHE2015INTERNATIONALJOINTCONFERENCEONNEURALNETWORKSIJCNN2015null. 2015, http://ir.ia.ac.cn/handle/173211/10768.
[13] Dong Qiulei, Kong Qingqun, Zeng Yi. Biologically Inspired Deep Stereo Model. PROCEEDINGS OF THE 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2015)null. 2015, http://ir.ia.ac.cn/handle/173211/12473.
[14] 孔庆群, 王波, 胡占义. 加权视差能量模型. 自动化学报[J]. 2014, 40(2): 227-235, http://sciencechina.cn/gw.jsp?action=detail.jsp&internal_id=5064045&detailType=1.
[15] Fan, Bin, Kong, Qingqun, Trzcinski, Tomasz, Wang, Zhiheng, Pan, Chunhong, Fua, Pascal. Receptive Fields Selection for Binary Feature Description. IEEE TRANSACTIONS ON IMAGE PROCESSING[J]. 2014, 23(6): 2583-2595, http://dx.doi.org/10.1109/TIP.2014.2317981.
[16] Fan, Bin, Huo, Chunlei, Pan, Chunhong, Kong, Qingqun. Registration of Optical and SAR Satellite Images by Exploring the Spatial Relationship of the Improved SIFT. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS[J]. 2013, 10(4): 657-661, https://www.webofscience.com/wos/woscc/full-record/WOS:000312101300003.
[17] 樊彬, Kong, Qingqun, Yuan, Xiaotong, Wang, Zhiheng, Pan, Chunhong. LEARNING WEIGHTED HAMMING DISTANCE FOR BINARY DESCRIPTORS. IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)null. 2013, 2395-2399, http://www.irgrid.ac.cn/handle/1471x/974840.
[18] 孔庆群, 高伟. 视差计算的层级模型. 中国科学 信息科学[J]. 2013, 43(9): 1111-1123, [19] Fan, Bin, Kong, Qingqun, Yuan, Xiaotong, Wang, Zhiheng, Pan, Chunhong, IEEE. LEARNING WEIGHTED HAMMING DISTANCE FOR BINARY DESCRIPTORS. 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)null. 2013, 2395-2399, [20] 孔庆群, 明雁声, 胡占义. 视皮层中的视差计算. 自动化学报[J]. 2011, 37(6): 645-657, http://lib.cqvip.com/Qikan/Article/Detail?id=38101929.

科研活动

   
科研项目
( 1 ) 利用信息处理的观点探究视网膜层次化结构的组织原理, 主持, 国家级, 2014-01--2017-12
( 2 ) 类脑认知功能计算模型, 参与, 部委级, 2015-07--2017-06
( 3 ) 大脑初级视觉系统解析仿真平台研究与应用验证, 参与, 省级, 2015-01--2016-12
( 4 ) 基于多脑区协同的认知计算研究与验证, 参与, 省级, 2016-01--2017-12
( 5 ) 大规模二值特征描述子的快速匹配方法研究, 参与, 国家级, 2016-01--2019-12
( 6 ) 面向鲁棒图像特征提取与描述的深度学习方法研究, 主持, 省级, 2020-01--2022-12
( 7 ) 基于多尺度可塑性的果蝇学习环路模拟和视觉-行为集群策略研究, 主持, 研究所(学校), 2019-12--2020-12
参与会议
(1)人工智能的发展及应用   启迪之星(新泰)启动仪式暨智能制造创新发展论坛   2017-12-13
(2)Biological Inspired Deep Stereo Model   2015年国际图像处理大会   2015-09-27