Wei Liu                                   刘威


Director of Computer Vision Center @ Tencent AI Lab


Thesis supervisor: Professor Shih-Fu Chang

Email: wliu at 

Google Scholar Page   


Research interests: machine learning, data mining, information retrieval, and

computer vision. My PhD thesis is dedicated to large-scale machine learning algorithms 

for classification and search. Currently, I am working on combinatorial, parallel, and distributed optimization methods to tackle noise/outlier present, high-dimensional, and large-scale learning problems. I am particularly interested in developing binary coding

and hashing techniques to handle big data.


     Resume   Publications   Courses   Collaborators
















l        Two papers are accepted as full oral by SIGIR 2017.

l        Four papers are accepted by CVPR 2017.

l        I act as an Associate Editor of Pattern Recognition Journal.

l        Our SIGIR’16 paper Discrete Collaborative Filtering won the Best Paper Award Honorable Mention!

l        We gave a tutorial of “Compact Features for Visual Search” at CVPR 2016.

l        Hashing by Deep Learning, which is a section of our hashing survey paper appearing in Proceedings of the IEEE 2016.

l        I acted as a guest editor of  Neurocomputing Special Issue on Advanced Learning for Large-Scale Heterogeneous Computing 2016.

l        I taught the Computer Vision course at Stevens Institute of Technology in 2015 Spring.

l        I acted as a guest editor of  ACM TIST Special Issue on Mobile Social Multimedia Analytics in the Big Data Era 2015.

l        I taught the Database Mining course at RPI in 2014 Fall.

l        One paper won the Best Paper Travel Award of IEEE International Symposium on Biomedical Imaging (ISBI), 2014.

l        I acted as a guest editor of Neurocomputing Special Issue on Learning for 3D Understanding 2015.

l        I won Jury Award for best thesis of Department of Electrical Engineering, Columbia University, 2013.

l        I passed my PhD defense on August 24th, 2012.

l        I won Josef Raviv Memorial Postdoctoral Fellowship 2012-2013. Thanks to IBM Research!

l        I was awarded Facebook Fellowship 2011-2012 (one of five winners worldwide). Thanks to Facebook!



Significant Publications


1.        Large-Scale Machine Learning for Classification and Search   Wei Liu. PhD Thesis 2012.

2.        Robust and Scalable Graph-Based Semisupervised Learning   Wei Liu et al. Proceedings of the IEEE 2012.

3.        Learning to Hash for Indexing Big Data ­ A Survey   Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang. Proceedings of the IEEE 2016. New!

4.        Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes   Guo-Jun Qi, Wei Liu, Charu Aggarwal, and Thomas Huang. TPAMI 2016. New!

5.        Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization   Yadong Mu, Wei Liu, Xiaobai Liu, and Wei Fan. IEEE TKDE 2017. New!

6.        Efficient Robust Conditional Random Fields   Dongjin Song, Wei Liu, Tianyi Zhou, Dacheng Tao, and David A. Meyer. IEEE TIP 2015.

7.        Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval   Xiaofan Zhang, Wei Liu, Murat Dundar, Sunil Badve, and Shaoting Zhang. IEEE TMI 2015.

8.        Discrete Collaborative Filtering   Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, and Tat-Seng Chua. SIGIR 2016. code New!

9.        Zeta Hull Pursuits: Learning Nonconvex Data Hulls   Yuanjun Xiong, Wei Liu, Deli Zhao, and Xiaoou Tang. NIPS 2014.

10.     Discrete Graph Hashing   Wei Liu et al. NIPS 2014 (spotlight oral).

11.     Compact Hyperplane Hashing with Bilinear Functions   Wei Liu et al. ICML 2012 (oral).

12.     Hashing with Graphs   Wei Liu et al. ICML 2011 (oral). code

13.     Large Graph Construction for Scalable Semi-Supervised Learning   Wei Liu et al. ICML 2010 (oral). code

14.     Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization   Wei Liu et al. KDD 2010 (full oral).

15.     Unsupervised One-Class Learning for Automatic Outlier Removal   Wei Liu et al. CVPR 2014 (oral).

16.     Supervised Hashing with Kernels   Wei Liu et al. CVPR 2012 (oral). code

17.     Noise Resistant Graph Ranking for Improved Web Image Search   Wei Liu et al. CVPR 2011.

18.     Robust Multi-Class Transductive Learning with Graphs   Wei Liu and Shih-Fu Chang. CVPR 2009. code

19.     Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation   Wei Liu et al. CVPR 2005 (oral).

20.     Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval   Steven C. H. Hoi, Wei Liu, and Shih-Fu Chang. CVPR 2008. code

21.     A Face Annotation Framework with Partial Clustering and Interactive Labeling   Yuandong Tian, Wei Liu, Rong Xiao, Fang Wen, and Xiaoou Tang. CVPR 2007.

22.     Learning Distance Metrics with Contextual Constraints for Image Retrieval   Steven C.H. Hoi, Wei Liu, Michael R. Lyu, and Wei-Ying Ma. CVPR 2006.

23.     Nonparametric Subspace Analysis for Face Recognition   Zhifeng Li, Wei Liu, Dahua Lin, and Xiaoou Tang. CVPR 2005.

24.     Face Recognition via Archetype Hull Ranking   Yuanjun Xiong, Wei Liu, Deli Zhao, and Xiaoou Tang. ICCV 2013.

25.     Learning Hash Codes with Listwise Supervision   Jun Wang, Wei Liu, Andy Sun, and Yu-Gang Jiang. ICCV 2013.

26.     Output Regularized Metric Learning with Side Information   Wei Liu et al. ECCV 2008.

27.     Spatio-temporal Embedding for Statistical Face Recognition from Video   Wei Liu et al. ECCV 2006.

28.     Low-Rank Similarity Metric Learning in High Dimensions   Wei Liu et al. AAAI 2015.

29.     Constrained Metric Learning via Distance Gap Maximization   Wei Liu et al. AAAI 2010.

30.     Spectral Kernel Learning for Semi-Supervised Classification   Wei Liu et al. IJCAI 2009.

31.     Bayesian Tensor Inference for Sketch-based Facial Photo Hallucination   Wei Liu et al. IJCAI 2007.





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