LIU, Wei
Adjunct Associate Professor

Education Background
PhD (Columbia University)
BS (Zhejiang University)

Research Field
AI, Machine Learning, Computer Vision, Information Retrieval, Big Data.
Email
wl2223@columbia.edu
Biography
Wei Liu is currently a Distinguished Scientist of Tencent and the director of Ads Multimedia AI at Tencent Data Platform. Prior to that, he received the Ph.D. degree in EECS from Columbia University, USA, and was a research scientist of IBM T. J. Watson Research Center, USA. Dr. Liu has long been devoted to fundamental research and technology development in core fields of AI. His research works win a number of awards and honors, such as the 2013 Jury Award for Best Thesis of Columbia University, the 2016 and 2017 SIGIR Best Paper Award Honorable Mentions, and the 2018 "AI's 10 To Watch" honor. Dr. Liu currently serves on the editorial boards of IEEE TPAMI, TNNLS, TCSVT, and is an Area Chair of NeurIPS, CVPR, ICCV, IJCAI, AAAI. Dr. Liu is a Fellow of IAPR (International Association for Pattern Recognition), AAIA (Asia-Pacific Artificial Intelligence Association), and BCS (British Computer Society), and is an Elected Member of ISI (International Statistical Institute).

Academic Publications
Journal
Yuesong Tian, Li Shen, Li Shen, Guinan Su, Zhifeng Li, and Wei Liu, “AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
Haibo Qiu, Dihong Gong, Zhifeng Li, Wei Liu, and Dacheng Tao, “End2End Occluded Face Recognition by Masking Corrupted Features”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
Jiangliu Wang, Jianbo Jiao, Linchao Bao, Shengfeng He, Wei Liu, and Yunhui Liu, “Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
Congliang Chen, Li Shen, Haozhi Huang, and Wei Liu, “Quantized Adam with Error Feedback”, ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 5, article 56, September 2021.
Zequn Jie, Peng Sun, Xin Li, Jiashi Feng, and Wei Liu, “Anytime Recognition with Routing Convolutional Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 43, no. 6, pp. 1875-1886, June 2021.
Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, and Wenwu Zhu, “Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
Wei Zhang, Bairui Wang, Lin Ma, and Wei Liu, “Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 42, no. 12, pp. 3088-3101, December 2020.
Zechun Liu, Wenhan Luo, Baoyuan Wu, Xin Yang, Wei Liu, and Kwang-Ting Cheng, “Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance”, International Journal of Computer Vision (IJCV), vol. 128, no. 1, pp. 202-219, 2020.
Yeqing Li, Wei Liu, and Junzhou Huang, "Sub-Selective Quantization for Learning Binary Codes in Large-Scale Image Search", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 40, no. 6, pp. 1526-1532, June 2018.
Xiao Wang, Shiqian Ma, Donald Goldfarb, and Wei Liu, "Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization", SIAM Journal on Optimization (SIOPT), vol. 27, no. 2, pp. 927-956, May 2017.
Wei Liu and Tongtao Zhang, "Multimedia Hashing and Networking", IEEE MultiMedia, vol. 23, no. 3, pp. 75-79, July-September 2016.
Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang, "Learning to Hash for Indexing Big Data - A Survey", Proceedings of the IEEE, vol. 104, no. 1, pp. 34-57, January 2016.
Wei Liu et al., "Robust and Scalable Graph-Based Semisupervised Learning", Proceedings of the IEEE, vol. 100, no. 9, pp. 2624-2638, September 2012.
?
Conference
Kaipeng Zhang, Zhenqiang Li, Zhifeng Li, Wei Liu, and Yoichi Sato, “Neural Routing by Memory”, in Advances in Neural Information Processing Systems (NeurIPS), vol. 34, 2021.
Xiaolong Yang, Xiaohong Jia, Dihong Gong, Dong-Ming Yan, Zhifeng Li, and Wei Liu, “LARNet: Lie Algebra Residual Network for Profile Face Recognition”, in Proc. International Conference on Machine Learning (ICML), 2021.
Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, and Wei Liu, “Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework”, in Proc. International Conference on Machine Learning (ICML), 2021.
Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu, “Generalizing Face Forgery Detection with High-frequency Features”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, and Wei Liu, “VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Long Chen, Zhihong Jiang, Jun Xiao, and Wei Liu, “Human-like Controllable Image Captioning with Verb-specific Semantic Roles”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Yuehua Zhu, Muli Yang, Cheng Deng, and Wei Liu, “Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies”, in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, 2020 (Spotlight Oral).
Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, and Wei Liu, “Adversarial Learning for Robust Deep Clustering”, in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, 2020.
Chao Li, Haoteng Tang, Cheng Deng, Liang Zhan, and Wei Liu, “Vulnerability vs. Reliability: Disentangled Adversarial Examples for Cross-Modal Learning”, in Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 (Full Oral).
Chao Li, Shangqian Gao, Cheng Deng, De Xie, and Wei Liu, “Cross-Modal Learning with Adversarial Samples”, in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019.
Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, and Wei Liu, “Learning to Compose Dynamic Tree Structures for Visual Contexts”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Oral, Best Paper Finalist).
Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, and Martial Hebert, “Image Deformation Meta-Networks for One-Shot Learning”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Oral, Best Paper Finalist).
Fangyu Zou, Li Shen, Zequn Jie, Weizhong Zhang, and Wei Liu, “A Sufficient Condition for Convergences of Adam and RMSProp”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Oral).
Yang Feng, Lin Ma, Wei Liu, and Jiebo Luo, “Unsupervised Image Captioning”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Lingxue Song, Dihong Gong, Zhifeng Li, Changsong Liu, and Wei Liu, “Occlusion Robust Face Recognition based on Mask Learning with Pairwise Differential Siamese Network”, in Proc. IEEE International Conference on Computer Vision (ICCV), 2019.
Yunzhe Tao, Qi Sun, Qiang Du, and Wei Liu, "Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling", Advances in Neural Information Processing Systems (NeurIPS), vol. 31, 2018.
Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, and Qi Wu, "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning", Advances in Neural Information Processing Systems (NeurIPS), vol. 31, 2018.
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu, “CosFace: Large Margin Cosine Loss for Deep Face Recognition”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Long Chen, Hanwang Zhang, Jun Xiao, Liqiang Nie, Jian Shao, Wei Liu, and Tat-Seng Chua, “SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua, "Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention", in Proc. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017 (Full Oral).
Fumin Shen, Yadong Mu, Yang Yang, Wei Liu, Li Liu, Jingkuan Song, and Heng Tao Shen, "Classification by Retrieval: Binarizing Data and Classifiers", in Proc. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017 (Full Oral, Best Paper Award Honorable Mention).
Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, and Tat-Seng Chua, "Discrete Collaborative Filtering", in Proc. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016 (Full Oral, Best Paper Award Honorable Mention).
Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen, “Supervised Discrete Hashing”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
Wei Liu et al., "Discrete Graph Hashing", Advances in Neural Information Processing Systems (NeurIPS), vol. 27, 2014 (Spotlight Oral).
Wei Liu et al., "Unsupervised One-Class Learning for Automatic Outlier Removal", in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Oral).
Wei Liu et al., "Compact Hyperplane Hashing with Bilinear Functions", in Proc. International Conference on Machine Learning (ICML), 2012.
Wei Liu et al., "Supervised Hashing with Kernels", in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012 (Oral).
Wei Liu et al., "Hashing with Graphs", in Proc. International Conference on Machine Learning (ICML), 2011.
Wei Liu et al., "Noise Resistant Graph Ranking for Improved Web Image Search", in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
Wei Liu et al., "Large Graph Construction for Scalable Semi-Supervised Learning", in Proc. International Conference on Machine Learning (ICML), 2010.
Wei Liu et al., "Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization", in Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010 (Full Oral).
Wei Liu and Shih-Fu Chang, "Robust Multi-Class Transductive Learning with Graphs", in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
Wei Liu et al., “Output Regularized Metric Learning with Side Information”, in Proc. European Conference on Computer Vision (ECCV), 2008.
Wei Liu et al., “Spatio-temporal Embedding for Statistical Face Recognition from Video”, in Proc. European Conference on Computer Vision (ECCV), 2006.
Wei Liu et al., “Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005 (Oral).
?