仲国强

2014年3月至今: 由中国海洋大学 青年英才工程 (第三层次) 项目引进, 副教授, 硕士生导师
2011.10-2013.7: 博士后, 导师: Mohamed Cheriet教授, 魁北克大学高等技术学院, 蒙特利尔, 加拿大
2009.9-2010.1: 访问学生, 导师: Dit-Yan Yeung教授, 香港科技大学, 香港, 中国
2007.9-2011.7: 工学博士, 导师: 刘成林研究员, 中国科学院自动化研究所, 北京, 中国
2004.9-2007.7: 理学硕士, 导师: 孟大志教授, 北京工业大学, 北京, 中国
2000.9-2004.7: 理学学士, 河北师范大学, 石家庄, 中国

2015.1至今: ACM会员, IEEE会员, IAPR会员, 中国计算机学会会员, 中国人工智能学会会员, 中国自动化学会会员, 中国图象图形学学会会员
2014.11至今: 中国人工智能学会模式识别专业委员会委员, 中国自动化学会模式识别与机器智能专委会委员, 中国图象图形学学会文档分析与识别专委会委员

会议主席: 在第十一届信息科学和信号处理及其应用国际会议(ISSPA 2012) 海报展示协同主席, 2014智能数据分析研讨会(IDAS2014)程序委员会主席, 2015智能数据分析研讨会(IDAS2015)程序委员会主席, 2016 国际神经网络联合会议基于深度学习的类脑计算与模式识别特别会议协同主席, 中国人工智能学会模式识别专家讲坛(第二届)程序委员会主席, 中国计算机学会计算机视觉专委会走进高校系列报告会(第十九届)程序委员会主席, 2017中法信息与通信计算研讨会宣传主席, 2017国际图形与图像处理会议出版协同主席;
会议程序委员会委员: 第十七届神经信息处理国际会议 (ICONIP 2010), ISSPA 2012, ICDAR2015, BICS2016, DLPR2016, CCPR2016, ICFHR2016, ICDAR2017;
期刊审稿人: IEEE TNNLS, IEEE TCSVT, Pattern Recognition, Neurocomputing, Neural Computing and applications, Cognitive Computation, 自动化学报, 计算机学报, 中国海洋大学学报;
会议审稿人: ICONIP 2010, ICONIP 2011, ISSPA 2012, CCPR2012, CCDM2014, BIC-TA 2015, ICRA2016, ICPR2016, CCPR2016, ICFHR2016, ICDAR2017.

1. 面向自然环境中文字检测与识别的深度网络精简技术研究, CCF-腾讯犀牛鸟创意基金, 项目主持人, 2018.1-2018.12. 
2. 服务于视障人群的智能穿戴设备研发, 青岛市产业培育计划科技惠民专项, 项目主持人, 2018.1-2019.12.
3. 监督的深度学习算法及其在海洋环境数据分析中的应用, 国家自然科学基金青年基金项目(NSFC), 项目主持人, 2015.1 -- 2017.12.
4. 深度学习和大规模数据挖掘算法及其应用研究, 中央高校基本科研业务费专项项目(青年英才工程启动经费), 项目主持人, 2014.3 -- 2019.2.
5. 深度小波网络模型及其应用研究, 模式识别国家重点实验室开放课题基金项目, 项目主持人, 2015.1 -- 2016.12. (已结题)
6. 海洋大数据分析预报技术研发, 国家重点研发计划项目, 参与.
7. 海洋科学研究中的范式转型与对策研究, 中国海洋发展研究会项目, 参与.
8. 科技期刊微信平台消息推送的分析与策略, 中国科学技术期刊编辑学会基金项目, 参与.

2014-2015, Pattern Recognition期刊优秀审稿人;
2015, 第五届“华为杯”中国大学生智能设计竞赛三等奖, 指导教师, 参赛队员: 杨攀, 蔡亚娟, 甘言海;
2015, 第五届“华为杯”中国大学生智能设计竞赛三等奖, 指导教师, 参赛队员: 郑煜辰, 周小伟, 石雅欣;
2016, 中国海洋大学“优秀教师”;
2016, 中国海洋大学“优秀班主任”;
2017, 中国海洋大学信息科学与工程学院“优秀共产党员”；
2017, 第七届“华为杯”中国大学生智能设计竞赛二等奖, 指导教师, 参赛队员: 魏洪旭, 王海珍, 孔浩;
2017, 第七届“华为杯”中国大学生智能设计竞赛华为专项奖, 指导教师, 参赛队员: 魏洪旭, 王海珍, 孔浩;
2017, 第七届“华为杯”中国大学生智能设计竞赛二等奖, 指导教师, 参赛队员: 张康, 凌霄, 洪辰;
2017, 第七届“华为杯”中国大学生智能设计竞赛华为专项奖, 指导教师, 参赛队员: 张康, 凌霄, 洪辰;
2017, 中国海洋大学本科毕业论文“优秀指导教师”.

[B1] 王勇, 仲国强, 孙鑫: 机器学习导论. 机械工业出版社, 2016.
[J10] Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: SLMOML: Online Metric Learning with Global Convergence. IEEE Transactions on Circuits and Systems for Video Technology (in press).
[J9] Partha Pratim Roy, Guoqiang Zhong, Mohamed Cheriet: Tandem HMMs Using Deep Belief Networks for Offline Handwriting Recognition. Frontiers of Information Technology and Electronic Engineering 18(7): 978-988 (2017).
[J8] Jianyuan Sun, Guoqiang Zhong, Junyu Dong, Hina Saeeda, Qin Zhang: Cooperative Profit Random Forests With Application in Ocean Front Recognition. IEEE Access 5: 1398-1408 (2017)
[J7] Qin Zhang, Jianyuan Sun, Guoqiang Zhong, Junyu Dong: Random Multi-Graphs: A Semi-Supervised Learning Framework for Classification of High Dimensional Data. Image and Vision Computing 60: 30-37 (2017)
[J6] Xiaopeng Liu, Guoqiang Zhong, Cong Liu, Junyu Dong: Underwater Image Colour Constancy Based on Deep Sparse Nonnegative Matrix Factorization. IET Image Processing 11(1): 38-43 (2017)
[J5] Heng Zhang, Guoqiang Zhong: Improving Short Text Classification by Learning Vector Representations of both Words and Hidden Topics. Knowl.-Based Syst. 102: 76-86 (2016)
[J4] Guoqiang Zhong, Mohamed Cheriet: Tensor representation learning based image patch analysis for text identification and recognition. Pattern Recognition 48(4) : 1207-1220 (2015).
[J3] Guoqiang Zhong, Mohamed Cheriet: Large margin low rank tensor analysis. Neural Computation 26(4) : 761-780 (2014).
[J2] Guoqiang Zhong, Cheng-Lin Liu: Error-correcting output codes based ensemble feature extraction. Pattern Recognition 46(4): 1091-1100 (2013).
[J1] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Joint learning of error-correcting output codes and dichotomizers from data. Neural Computing and Applications 21(4): 715-724 (2012). 

[C25] Qin Zhang, Junyu Dong, Guoqiang Zhong: Visual Texture Perception via Graph-based Semi-supervised Learning. ICGIP, 2017.
[C24] Guoqiang Zhong, Hongxu Wei, Yuchen Zheng, Junyu Dong: Marginal Deep Architectures. ACPR, 2017.
[C23] Yanhai Gan, Huifang Chi, Ying Gao, Jun Liu, Guoqiang Zhong, Junyu Dong: Perception Driven Texture Generation. ICME, 2017.
[C22] Xiaowei Zhou, Guoqiang Zhong, Lin Qi, Junyu Dong,Tuan D. Pham, Jianzhou Mao: Surface Height Map Estimation from a Single Image Using Convolutional Neural Networks. ICGIP, 2016.
[C21] Xuecheng Han, Hui Yao, Guoqiang Zhong: Handwritten Text Line Segmentation by Spectral Clustering. ICGIP, 2016.
[C20] Guoqiang Zhong, Hui Yao, Yutong Liu, Chen Hong, Tuan Pham: Classification of Photographed Document Images Based on Deep-Learning Features. ICGIP, 2016.
[C19] Guoqiang Zhong, Xiao Ling: The necessary and sufficient conditions for the existence of the optimal solution of trace ratio problems. CCPR, 2016.
[C18] Guoqiang Zhong, Yaxin Shi, Mohamed Cheriet: Relational Fisher Analysis: A General Framework for Dimensionality Reduction. IJCNN, 2016
[C17] Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: Scalable Large Margin Online Metric Learning. IJCNN, 2016.
[C16] Guoqiang Zhong, Hui Xu, Pan Yang, Sijiang Wang, Junyu Dong: Deep Hashing Learning Networks. IJCNN, 2016.
[C15] Jianwen Lou, Lin Qi, Junyu Dong, Hui Yu, Guoqiang Zhong: Learning Perceptual Texture Similarity and Relative Attributes from Computational Features. IJCNN, 2016.
[C14] Pan Yang, Haoran Zhao, Lin Qi, Guoqiang Zhong: Self-taught recovery of depth data. APSIPA 2015: 1270-1275.
[C13] Yajuan Cai, Guoqiang Zhong, Yuchen Zheng, Kaizhu Huang, Junyu Dong: Is DeCAF Good Enough for Accurate Image Classification? ICONIP (2) 2015: 354-363.
[C12] Yuchen Zheng, Yajuan Cai, Guoqiang Zhong, Youssouf Chherawala, Yaxin Shi, Junyu Dong: Stretching deep architectures for text recognition. ICDAR 2015: 236-240.
[C11] Guoqiang Zhong, Xin Mao, Yaxin Shi, Junyu Dong: 3D Texture Recognition for RGB-D Images. CAIP (2) 2015: 518-528.
[C10] Chengcheng Jia, Guoqiang Zhong, Yun Raymond Fu: Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery. AAAI 2014: 1228-1234.
[C9] Yuchen Zheng, Guoqiang Zhong, Jun Liu, Xiaoxu Cai, Junyu Dong: Visual Texture Perception with Feature Learning Models and Deep Architectures. CCPR 2014.
[C8] Guoqiang Zhong, Mohamed Cheriet: An empirical evaluation of supervised dimensionality reduction for recognition. ICDAR 2013.
[C7] Guoqiang Zhong, Mohamed Cheriet: Adaptive error-correcting output codes. IJCAI 2013.
[C6] Guoqiang Zhong, Mohamed Cheriet: Image patches analysis for text block identification. ISSPA 2012: 1241-1246.
[C5] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Low rank metric learning with manifold regularization. ICDM 2011: 1266-1271.
[C4] Guoqiang Zhong, Wu-Jun Li, Dit-Yan Yeung, Xinwen Hou, Cheng-Lin Liu: Gaussian process latent random field. AAAI 2010.
[C3] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Learning ECOC and dichotomizers jointly from data. ICONIP (1) 2010: 494-502.
[C2] Guoqiang Zhong, Xinwen Hou, Cheng-Lin Liu: Relative distance-based Laplacian eigenmaps. CJKPR, 2009.
[C1] Guoqiang Zhong, Lina Wang, Dazhi Meng: The differential geometrical method of modifying SVM and its application in Intron-Extron classification. CCPR, 2007. 

[BC3] Guoqiang Zhong, Mohamed Cheriet: Low Rank Tensor Manifold Learning. Low-Rank and Sparse Modeling for Visual Analysis, 2014.
[BC2] Mohamed Cheriet, Reza Farrahi Moghaddam, Ehsan Arabnejad Guoqiang Zhong: Manifold learning for the shape-based recognition of historical Arabic documents. Handbook of Statistics: Applications of Machine Learning, 2012.
[BC1] Guoqiang Zhong, Kaizhu Huang, Xinwen Hou, Shiming Xiang: Local tangent space Laplacian eigenmaps. Computational Intelligence, 2012.
Bx: 专著; Jx: 期刊 x; Cx: 会议 x; BCx: 书的章节 x.