斯博国际app


教师信息                                                                                                                                                 个人照片      
姓    名赵仲秋性    别 男
出生年月1977年9月最终学位博士                  
毕业学校中国科技大学
从事专业模式识别与智能系统                    职    务博士生导师
所属院系计算机科学与技术系
所属科室(研究所)计算机软件与理论研究所                     职     称 研究员
联系方式

办公电话:0551-62902575

E-mail: zhongqiuzhao AT gmail DOT com

通讯地址

邮  编
简    历

江苏靖江人;工学博士,研究员,博士生导师;IEEE/ACM会员。

-2007年12月~2008年3月在斯博国际app“图像信息处理研究室”从事研究工作;
-2008年4月~2009年11月于法国 UNIVERSITE PAUL CEZANNE – AIX-MARSEILLE III 任博士后;
-2009年12月至今,于斯博国际app工作,2011年12月入选斯博国际app人才培育“C计划”。
-2012年入选“香江学者”计划,2013年1月~2014年12月于香港浸会大学计算机科学系从事合作研究。

近年来主持国家自然科学基金项目3项,教育部高等学校博士点新教师基金、中国博士后科学基金特别资助等各1项;承担科技部863项目、973课题、973前期预研专项项目、法国国家科研署(ANR)项目等多个项目的研究。

在相关领域已经发表论文50余篇,包括IEEE TNNLSIEEE TIPIEEE ToCIEEE TKDEPRIEEE MultimediaNeurocomputingPRL 等权威学术期刊,以及IJCAIECCVACM MIRACCVICIP 等权威学术会议。获授权国家发明专利5项。

担任IEEE Trans. Evolutionary Computation, IEEE Trans. Multimedia, IEEE Trans. Image Processing, IEEE TNNLS,Computer Vision and Image Understanding,Pattern Recognition Letters, 《计算机学报》等期刊的论文审稿人。

研究方向

研究方向包括:模式识别、深度学习、图像视频分类与理解、数据挖掘等。

欢迎具有良好数学基础、有志于从事图像和视频处理技术应用研究的学生报考研究生!理想和态度决定您的高度!

主持科研项目:

(1)安徽省杰出青年科学基金,图像分类和标注中的稀疏感知问题No. 170808J082017.1.1-2019.12

(2)国家自然科学基金面上项目,“增长的卷积神经网络模型中的若干关键问题研究”,

   (No. 61672203,2017.1-2020.12

(3)国家自然科学基金面上项目,“基于耦合判别和协作稀疏表示的图像表征和标注研究”,

   (No. 61375047,2014.1 - 2017.12)

(4)“香江学者”计划,“高维数据下的特征选择及应用”,

   (No. XJ2012012,2013.1 - 2014.12)

(5)国家自然科学基金青年基金,“约束最大差异投影在基于内容的多样化图像检索中的应用研究”

   (No. 61005007, 2011.1 - 2013.12)

(6)教育部高等学校博士点新教师基金,“基于子域模块分类器的非对称模式分类研究”

   (No. 200803591024,2009.1 - 2011.12)

教学工作

《人工神经网络》 
《智能信息处理》 
《程序设计基础》
获奖情况

2012年入选“香江学者”计划;
2014年获ACM南京分会(江苏、安徽地区)卓越青年科学家奖提名奖;
2016年获安徽省杰出青年科学基金资助;
2016年获教育部自然科学一等奖;

2018年获吴文俊人工智能科学技术奖科技进步一等奖

主要论著
 

[28] Z.Q. Zhao, P. Zheng,S. Xu, X. Wu, , DOI: 10.1109/TNNLS.2018.2876865, IEEE Transactions on Neural Networks and Learning Systems, 2019.   

[27] Z.Q. Zhao, J. Hu; W. Tian, N. Ling, Cooperative Adversarial Network for Accurate Super Resolution, Asian Conference on Computer Vision (ACCV), 2018.

[26] P. Zheng, Z.Q. Zhao*, J. Gao, X. Wu, A set-level joint sparse representation for image set classification, Information Sciences, Vol. 448–449,pp.75–90, June 2018. (*corresponding author

[25] P. Zheng, Z.Q. Zhao*, J. Gao, X. Wu, Image set classification based on cooperative sparse representation, Pattern Recognition, Volume 63, Pages 206–217,March 2017. (*corresponding author)

[24] D. Hu, X. Zhang, Y. Fan, Z.Q. Zhao, L. Wang, X. Wu, X. Wu,  On Digital Image Trustworthiness, Applied Soft Computing, Vol. 48, pp.240-253, 2016. 

[23] Z.Q. Zhao, Y. Cheung, H. Hu, X. Wu, Corrupted and Occluded Face Recognition via Cooperative Sparse Representation, Pattern Recognition, Vol. 56, Pages 77–87, August, 2016.

[22] S. Li, Z.H. You, H. Guo, X. Luo, Z.Q. Zhao, Inverse-Free Extreme Learning Machine With Optimal Information Updating, IEEE Transactions on Cybernetics, vol.46,issue 5, pp.1229-1241, 2016.
[21] 
Z.Q. Zhao, Y. Cheung, H. Hu, X. Wu, Expanding dictionary for robust face recognition: pixel is not necessary while sparsity is, IET Computer Vision, Vol. 9(5),pp.648 –654, 2015.

[20] X. Wu, H. Chen, G.Q. Wu, J. Liu, Q. Zheng, X. He, A. Zhou, Z.Q. Zhao, B. Wei, Y. Li, Q. Zhang, S. Zhang: Knowledge Engineering with Big Data.IEEE Intelligent Systems, 30(5): 46-55 (2015)
[19] 
Z.Q. Zhao, Y. Hong, P. Zheng, X. Wu: Plant identification using triangular representation based on salient points and margin points. ICIP 2015: 1145-1149

[18] Z.Q. Zhao, L.H. Ma, Y. Cheung, X. Wu, Y. Tang, C.L.P. Chen, ApLeaf: An efficient android-based plant leaf identification system, Neurocomputing, Volume 151, Part 3, 3 March 2015, Pages 1112-1119.

[17] J. Wang, M. Wang, P.P. Li, L. Liu, Z.Q. Zhao, X. Hu, X. Wu: Online Feature Selection with Group Structure Analysis. IEEE Transactions on Knowledge and Data Engineering, 27(11): 3029-3041 (2015)

[16] Z.Q. Zhao, B.J. Xie, Y. Cheung, X. Wu, Plant Leaf Identification via A Growing Convolution Neural Network with Progressive Sample Learning, ACCV, 2014.

[15] 赵仲秋, 季海峰, 高隽, 胡东辉, 吴信东. 基于稀疏编码多尺度空间潜在语义分析的图像分类,《计算机学报》, 37(6): 1251-1260, 2014.

[14] Z.Q. Zhao, XinDong Wu, CanYi Lu, Herve Glotin, Jun Gao, Optimizing widths with PSO for center selection of Gaussian radial basis function networks,SCIENCE CHINA Information Sciences, Volume 57, Issue 5, pp 1-17, May 2014. DOI: 10.1007/s11432-013-4850-5

[13] Bo Li, Jin Liu, Z.Q. Zhao and Wen-Sheng Zhang, Locally Linear Representation Fisher Criterion, International Joint Conference on Neural Networks (IJCNN), 2013. 

[12] J. Wang, Z.Q. Zhao, X. Hu, Y. Cheung, M. Wang, and X. Wu, Online Group Feature Seclection, 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013. 

[11] Z.Q. Zhao, H. Glotin, Z. Xie, J. Gao, and X. Wu, Cooperative Sparse Representation in Two Opposite Directions for Semi-supervised Image Annotation, IEEE Transactions on Image Processing (TIP), Vol. 21 , Issue 9, pp. 4218 - 4231, 2012 (regular paper).

[10] Can-Yi Lu, Hai Min, Zhong-Qiu Zhao, Lin Zhu, De-Shuang Huang, Shuicheng Yan, Robust and Efficient Subspace Segmentation via Least Squares Regression,European Conference on Computer Vision (ECCV), 2012.(top conference)

[9]  Z.Q. Zhao, J.Z. Li , J. Gao, X.D. Wu, “A Modified Semi-Supervised Learning Algorithm on Laplacian Eigenmaps,” Neural Processing Letters,  vol. 32(1), 76-82, 2010. 

[8]  Z.Q. Zhao, J. Gao, H. Glotin, X.D. Wu, “A matrix modular neural network based on task decomposition with subspace division by adaptive affinity propagation clustering,”Applied Mathematical Modelling, 34, pp. 3884–3895, 2010. 

[7]  H. Glotin, Z.Q. Zhao, J. Gao, X.D. Wu, “A Matrix Modular SVM Robust to Imbalanced Dataset for Efficient Visual Concept Detections,” The 11th ACM SIGMM International Conference on Multimedia Information Retrieval (ACM MIR 2010), March 29-31, 2010, National Constitution Center, Philadelphia, Pennsylvania, USA. 
[6]  
Z.Q. Zhao, H. Glotin, “Diversifying Image Retrieval by Affinity Propagation Clustering on Visual Manifolds,” IEEE Mutimedia, vol. 16, no. 4, pp. 34-43, 2009. 
[5]  
Z.Q. Zhao, “A Novel Modular Neural Network for Imbalanced Classification Problems,” Pattern Recognition Letters, Vol.30, No.9, pp. 783-788, 2009. 
[4]  
Z.Q. Zhao, D.S. Huang, and W. Jia, “Palmprint Recognition with 2DPCA+PCA Based on Modular Neural Networks,” Neurocomputing,Vol. 71(1-3), pp. 448-454, 2007. 
[3]  
Z.Q. Zhao and D.S. Huang, “A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability,”Applied Mathematics Modelling,Vol. 31(7), pp. 1271-1281, 2007. 
[2]  
Z.Q. Zhao, D.S. Huang, B. Y.  Sun,  “Human face recognition based on multiple features using neural networks committee,”  Pattern Recognition Letters, Vol.25(12), pp.1351-1358, 2004.

[1]  H. Glotin, Z.Q. Zhao, S. Ayache, “Efficient Image Concept Indexing by Harmonic and Arithmetic Profiles Entropy,” Proceedings of 2009 IEEE International Conference on Image Processing (ICIP 2009), IEEE Signal Processing Society, pp.277-280, 2009