Director of Computer Vision Center @ Tencent AI Lab
Thesis supervisor: Professor Shih-Fu Chang
Email: wl2223 at columbia.edu or wliu.cu at gmail.com
Research interests: machine learning, computer vision, big data, information retrieval,
and optimization. I am particularly interested in developing binary coding and hashing
techniques to handle big, in both dimension and scale, data. I currently focus on
inventing deep spatio-temporal models to tackle a variety of multimedia AI problems.
l I serve as an Area Chair of ICDM 2018, ACM Multimedia 2018 and ICPR 2018, and a Senior PC of IJCAI 2018.
l Since Jan 2018, I act as an Associate Editor of Journal of Visual Communication and Image Representation.
l Since Jan 2018, I act as an Associate Editor of IEEE Transactions on Circuits and Systems for Video Technology.
l Since Mar 2017, I act as an Associate Editor of Pattern Recognition Journal.
l Our SIGIR’17 paper Classification by Retrieval: Binarizing Data and Classifiers 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 ACM TIST Special Issue on Mobile Social Multimedia Analytics in the Big Data Era 2017.
l I acted as a guest editor of Neurocomputing Special Issue on Advanced Learning for Large-Scale Heterogeneous Computing 2016.
l I acted as a guest editor of Neurocomputing Special Issue on Learning for 3D Understanding 2015.
l I taught the Computer Vision course at Stevens Institute of Technology in 2015 Spring.
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 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!
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.
4. Multi-label Learning with Missing Labels using Mixed Dependency Graphs Baoyuan Wu, Fan Jia, Wei Liu, Bernard Ghanem, and Siwei Lyu. IJCV 2018.
5. Sub-Selective Quantization for Learning Binary Code in Large-Scale Image Search Yeqing Li, Wei Liu, and Junzhou Huang. IEEE TPAMI 2018.
6. Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes Guo-Jun Qi, Wei Liu, Charu Aggarwal, and Thomas Huang. IEEE TPAMI 2017.
7. Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization Yadong Mu, Wei Liu, Xiaobai Liu, and Wei Fan. IEEE TKDE 2017.
8. Classification by Retrieval: Binarizing Data and Classifiers Fumin Shen, Yadong Mu, Yang Yang, Wei Liu, Li Liu, Jingkuan Song, and Heng Tao Shen. SIGIR 2017.
10. Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, and Xiaoou Tang. NIPS 2014.
11. Discrete Graph Hashing Wei Liu et al. NIPS 2014 (spotlight oral).
12. Compact Hyperplane Hashing with Bilinear Functions Wei Liu et al. ICML 2012 (oral).
14. Large Graph Construction for Scalable Semi-Supervised Learning Wei Liu et al. ICML 2010 (oral). code
15. GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization Li Shen, Wei Liu, Ganzhao Yuan, and Shiqian Ma. ICML 2017 (oral).
16. Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization Wei Liu et al. KDD 2010 (full oral).
17. Unsupervised One-Class Learning for Automatic Outlier Removal Wei Liu et al. CVPR 2014 (oral).
19. Noise Resistant Graph Ranking for Improved Web Image Search Wei Liu et al. CVPR 2011.
20. Robust Multi-Class Transductive Learning with Graphs Wei Liu and Shih-Fu Chang. CVPR 2009. code
21. Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation Wei Liu et al. CVPR 2005 (oral).
22. Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval Steven C. H. Hoi, Wei Liu, and Shih-Fu Chang. CVPR 2008. code
23. A Face Annotation Framework with Partial Clustering and Interactive Labeling Yuandong Tian, Wei Liu, Rong Xiao, Fang Wen, and Xiaoou Tang. CVPR 2007.
24. Learning Distance Metrics with Contextual Constraints for Image Retrieval Steven C.H. Hoi, Wei Liu, Michael R. Lyu, and Wei-Ying Ma. CVPR 2006.
25. Nonparametric Subspace Analysis for Face Recognition Zhifeng Li, Wei Liu, Dahua Lin, and Xiaoou Tang. CVPR 2005.
26. Face Recognition via Archetype Hull Ranking Yuanjun Xiong, Wei Liu, Deli Zhao, and Xiaoou Tang. ICCV 2013.
27. Learning Hash Codes with Listwise Supervision Jun Wang, Wei Liu, Andy Sun, and Yu-Gang Jiang. ICCV 2013.
28. Output Regularized Metric Learning with Side Information Wei Liu et al. ECCV 2008.
29. Spatio-temporal Embedding for Statistical Face Recognition from Video Wei Liu et al. ECCV 2006.
30. Low-Rank Similarity Metric Learning in High Dimensions Wei Liu et al. AAAI 2015.
31. Constrained Metric Learning via Distance Gap Maximization Wei Liu et al. AAAI 2010.
32. Spectral Kernel Learning for Semi-Supervised Classification Wei Liu et al. IJCAI 2009.
33. Bayesian Tensor Inference for Sketch-based Facial Photo Hallucination Wei Liu et al. IJCAI 2007.
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