Basic Information

 

         Deyu Meng (孟德宇

          副教授
          Associate Professor

          西安交通大学
          数学与统计学院
          信息与系统科学研究所
          西安,陕西,710049

           Institute for Information and System Sciences

           Faculty of Mathematics and Statistics

          Xi’an Jiaotong University 

           Xi’an, Shaan’xi Province, P. R. China, 710049

 

           Fax(传真):   +86 29 8266 8559 
           Tel(电话):  +86 29 8266 3153

 

XJTU Homepage(西安交通大学主页):     http://gr.xjtu.edu.cn/web/dymeng

CMU Homepage(卡内基梅隆大学主页): http://www.cs.cmu.edu/~deyum

Email(邮箱): dymeng at mail dot xjtu dot edu dot cn

  

Technical Program Committee:
ICML 2015; ACM MM 2014; ICPR 2014; ACCV 2014; 中国计算机大会(CNCC)2014 and others.

 

Journal Reviewer:
IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Transactions on Image Processing; IEEE Transactions on Neural Networks and Learning Systems; IEEE Transactions on Automation Science and Engineering; IEEE Transactions on SMC-B; IEEE Transactions on cybernetics; Signal Processing; Neural Computing and Applications; International Journal of Machine Learning and Cybernetics; Computers & Mathematics with Applications; Knowledge and Information Systems; International Journal of Biomathematics; Infrared Physics & Technology; Information Sciences; International Journal of Remote Sensing; Neurocomputing; 中国科学F辑:信息科学;计算机学报; 电子学报; 模式识别与人工智能; 自动化学报 and others.

 

NEW

Our MoG-LRMF code proposed in ICCV2013 is now available.

Deyu Meng, Fernando De la Torre. Robust Matrix Factorization with Unknown Noise. ICCV, 2013. Matlab code.

The code for our RPCA method on detecting small targets from a single infrared image is now available.

Chenqiang Gao, Deyu Meng, Yi Yang, Yongtao Wang, Xiaofang Gao, Alexander G. Hauptmann. Infrared Patch-image Model for Small Target Detection in A Single Image. IEEE Transactions on Image Processing. 22(12): 4996-5009, 2013. Matlab code.

Our SPLD method gets the best MAP performance on the Hollywood2 and Olympic Sports datasets.

Lu Jiang, Deyu Meng, Shoou-I Yu, Zhen-Zhong Lan, Shiguang Shan, Alexander Hauptmann. Self-paced Learning with Diversity. NIPS, 2014. Summlementary material.

Our SPaR method achieves the best result on the Multimedia Event Detection zero-example search.

Lu Jiang, Deyu Meng, Teruko Mitamura, Alexander Hauptmann. Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search. ACM MM. 2014. Slides.

Welcome to experience the performance of our newly published method on MSI denoising :

Yi Peng, Deyu Meng, Zongben Xu, Chenqiang Gao, Yi Yang, Biao Zhang. Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image DenoisingSupplementary Material, CVPR, 2014. Matlab code.

Our robust PCA method can well fit more complex noise beyond conventional Gaussian, Laplacian, Sparse noise or any combinations of them: (best student poster runner-up in MLA2014)

Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, Lei Zhang. Robust principal component analysis with complex noiseSupplementary MaterialICML, 2014Matlab code

Our proposed FastMMD method decreases the time complexity of MMD calculation from conventional O(N^2d) to O(Nlog(d)) (minor revision in Neural Computation):

Ji Zhao, Deyu Meng. FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test, arXiv:1405.2664, 2014, Matlab Code

The code and slides of our ICMR best-paper-runner-up work are now available:

 Lu Jiang, Wei Tong, Deyu Meng, Alexander G. Hauptmann.Towards Efficient Learning of Optimal Spatial Bag-of-Words Representations. ICMR, 2014. slides, Please download our code in JS Tiling webpage

The corrected proof to the global optimum of weighted nuclear norm minimization:

Qi Xie, Deyu Meng, Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng, Zongben Xu. On the optimal solution of weighted nuclear norm minimization, arXiv:1405.6012v1, 2014.

Our new method can effectively parse human motion in unconstrained Internet videos WITHOUT need to label any videos for training:

Haoquan Shen, Shoou-I Yu, Yi Yang, Deyu Meng, Alexander Hauptmann. Unsupervised Video Adaptation for Parsing Human Motion. ECCV, 2014. [Project Page] [Code and Dataset] [Demo Video]

 

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