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WU, Xiao-Ming

Associate Professor in Computing@PolyU HK

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About Me

I am currently an associate professor at the Department of Computing, The Hong Kong Polytechnic University. I obtained my PhD degree from the Department of Electrical Engineering, Columbia University, advised by Prof. Shih-Fu Chang, with my thesis titled as "Learning on Graphs with Partially Absorbing Random Walks: Theory and Practice". An industrial application of my thesis research is App Push Recommendation in Huawei App Store. Prior to that, I studied at the Department of Information Engineering, The Chinese University of Hong Kong and obtained an MPhil degree, advised by Prof. Shuo-Yen Robert Li and Prof. Anthony Man-Cho So. Before coming to Hong Kong, I studied in Peking University, where I received my BSc degree from the School of Mathematical Sciences and my MSc degree from the Instituite of Computer Science and Technology, advised by Prof. Zongming Guo.

Research

At my research group, we are passionate about exploring the frontiers of artificial intelligence (AI) and developing advanced AI models to address challenges and applications across various domains including natural language processing, computer vision, information retrieval, and healthcare. Recently, our primary areas of focus include:

The projects we have worked on include:

Selected Publications

    __ indicates my current or former student, RA, or Postdoc, * indicates equal contribution, and # indicates corresponding author (PI).

  • ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
    Qijiong Liu, Nuo Chen, Tetsuya Sakai, Xiao-Ming Wu#
    To Appear in Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM) , March 2024.
    [PDF] [Code]

  • Towards LLM-driven Dialogue State Tracking
    Yujie Feng, Zexin Lu, Bo Liu, Li-Ming Zhan, Xiao-Ming Wu#
    To Appear in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Long Paper), December 2023.
    [PDF] [Code]

  • Real-World Image Super-Resolution as Multi-Task Learning
    Wenlong Zhang, Xiaohui Li*, Guangyuan Shi*, Xiangyu Chen, Yu Qiao, Xiaoyun Zhang, Xiao-Ming Wu#, Chao Dong#
    To Appear in Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), December 2023.
    [PDF] [Code]

  • Adaptive Graph Convolution Methods for Attributed Graph Clustering
    Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu, Xianchao Zhang
    In IEEE Transactions on Knowledge and Data Engineering (TKDE), May 2023. (Extension of the IJCAI 2019 paper)
    [PDF] [Code]

  • Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
    Haode Zhang, Haowen Liang, Li-Ming Zhan, Xiao-Ming Wu#, Albert Y.S. Lam
    In Findings of the 61st Annual Meeting of the Association for Computational Linguistics (Findings of ACL) (Long Paper), July 2023.
    [PDF] [Code]

  • Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
    Li Xu, Bo Liu, Ameer Hamza Khan, Lu Fan, Xiao-Ming Wu#,
    In Proceedings of the AHLI Conference on Health, Inference, and Learning 2023 (CHIL), June 2023. (Oral Presentation)
    [PDF] [Code]

  • Neighborhood-based Hard Negative Mining for Sequential Recommendation
    Lu Fan, Jiashu Pu, Rongsheng Zhang, Xiao-Ming Wu#,
    In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (Short Paper), July 2023.
    [PDF] [Code]

  • Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning
    Guangyuan Shi, Qimai Li, Wenlong Zhang, Jiaxin Chen, Xiao-Ming Wu#
    In Proceedings of the Eleventh International Conference on Learning Representations (ICLR), May 2023.
    [PDF] [Code]

  • FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation
    Qijiong Liu, Jieming Zhu, Jiahao Wu, Tiandeng Wu, Zhenhua Dong, Xiao-Ming Wu#,
    In Proceedings of the ACM Web Conference 2023 (WWW) (Research Track, Full Paper), April 2023.
    [PDF] [Code]

  • Medical Visual Question Answering via Conditional Reasoning and Contrastive Learning
    Bo Liu, Li-Ming Zhan, Li Xu, Xiao-Ming Wu#
    In IEEE Transactions on Medical Imaging (TMI), December, 2022. (Extension of the ACM MM 2020 paper)
    [PDF] [Code]

  • Continual Graph Convolutional Network for Text Classification
    Tiandeng Wu*, Qijiong Liu*, Yi Cao, Yao Huang, Xiao-Ming Wu#, Jiandong Ding #
    In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), February 2023.
    [PDF] [Code]

  • Boosting Few-shot Text Classification via Distribution Estimation
    Han Liu, Feng Zhang, Xiaotong Zhang#, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang
    In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), February 2023.
    [PDF] [Code]

  • A Closer Look at Few-Shot Out-of-Distribution Intent Detection
    Li-Ming Zhan, Haowen Liang, Lu Fan, Xiao-Ming Wu#, Albert Y.S. Lam
    In Proceedings of the 28th International Conference on Computational Linguistics (COLING) (Long Paper), October 2022. (Oral Presentation)
    [PDF] [Code]

  • Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation
    Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu#
    In Proceedings of the 28th International Conference on Computational Linguistics (COLING) (Long Paper), October 2022. (Oral Presentation)
    [PDF] [Code]

  • Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
    Haode Zhang, Haowen Liang, Yuwei Zhang, Li-Ming Zhan, Xiao-Ming Wu#, Xiaolei Lu, Albert Y.S. Lam
    In Proceedings of 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) (Long Paper), July 2022. (Oral Presentation)
    [PDF] [Code]

  • Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks
    Quanyu Dai, Xiao-Ming Wu#, Lu Fan, Qimai Li, Han Liu, Xiaotong Zhang, Dan Wang#, Guli Lin, Keping Yang
    In Pattern Rocognition Journal (PR), 2022.
    [PDF] [Code]

  • New Intent Discovery with Pre-training and Contrastive Learning
    Yuwei Zhang, Haode Zhang, Li-Ming Zhan, Xiao-Ming Wu#, Albert Y.S. Lam
    In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL) (Long Paper), May 2022
    [PDF] [Code]

  • Modeling User Behavior with Graph Convolution for Personalized Product Search
    Lu Fan*, Qimai Li*, Bo Liu, Xiao-Ming Wu#, Xiaotong Zhang, Fuyu Lv, Guli Lin, Sen Li, Taiwei Jin, Keping Yang
    In Proceedings of the ACM Web Conference 2022 (WWW) (Research Track, Full Paper), April 2022.
    [PDF] [Code]

  • Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
    Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen#, Dan Wang
    In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.
    [PDF] [Code]

  • Online-updated High-order Collaborative Networks for Single Image Deraining
    Cong Wang, Jinshan Pan, Xiao-Ming Wu#
    In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), February 2022.
    [PDF] [Code]

  • Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
    Guangyuan Shi*, Jiaxin Chen*, Wenlong Zhang, Li-Ming Zhan, Xiao-Ming Wu#
    In Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), December 2021. (Spotlight Presentation)
    [PDF] [Code]

  • Effectiveness of Pre-training for Few-shot Intent Classification
    Haode Zhang*, Yuwei Zhang*, Li-Ming Zhan, Jiaxin Chen, Guangyuan Shi, Xiao-Ming Wu#, Albert Y.S. Lam
    In Findings of the 2021 Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP) (Short Paper), November 2021.
    [PDF] [Code]

  • Contrastive Pre-training and Representation Distillation for Medical Visual Question Answering Based on Radiology Images
    Bo Liu, Li-Ming Zhan, Xiao-Ming Wu#
    In Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2021.
    [PDF] [Code]

  • Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs
    Qimai Li*, Xiaotong Zhang*, Han Liu*, Quanyu Dai, Xiao-Ming Wu#
    In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) (Research Track), August 2021.
    [PDF] [Code]

  • Embedding-based Product Retrieval in Taobao Search
    Sen Li, Fuyu Lv, Taiwei Jin, Guli Lin, Keping Yang, Xiaoyi Zeng, Xiao-Ming Wu, Qianli Ma
    In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) (Applied Data Science Track), August 2021.
    [PDF] [Code]

  • Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training
    Li-Ming Zhan, Haowen Liang*, Bo Liu*, Lu Fan, Xiao-Ming Wu#, Albert Y.S. Lam
    In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL) (Long Paper), August 2021. (Oral Presentation)
    [PDF] [Code ]

  • SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
    Bo Liu, Li-Ming Zhan*, Li Xu*, Lin Ma, Yan Yang, Xiao-Ming Wu#
    In Proceedings of the 2021 IEEE International Symposium on Biomedical Imaging (ISBI), April 2021. (Oral Presentation)
    [PDF] [Dataset ]

  • A Closer Look at the Training Strategy for Modern Meta-Learning
    Jiaxin Chen, Xiao-Ming Wu#, Yanke Li, Qimai Li, Li-Ming Zhan, Fu-lai Chung#
    In Proceedings of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), December 2020.
    [PDF] [Supplemental]

  • Medical Visual Question Answering via Conditional Reasoning
    Li-Ming Zhan*, Bo Liu*, Lu Fan, Jiaxin Chen, Xiao-Ming Wu#
    In Proceedings of the 28th ACM International Conference on Multimedia (ACM MM), October 2020.
    [PDF] [Code ]

  • M2GRL: A Multi-task Multi-view Graph Representation Learning Framework forWeb-scale Recommender Systems
    Menghan Wang#, Yujie Lin, Guli Lin, Keping Yang, Xiao-Ming Wu
    In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) (Applied Data Science Track), August 2020. (Oral Presentation)
    [PDF] [Code]

  • Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification
    Guangfeng Yan*, Lu Fan*, Qimai Li*, Han Liu, Xiaotong Zhang, Xiao-Ming Wu#, Albert Y.S. Lam
    In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL) (Long Paper), July 2020.
    [PDF] [ Code ]

  • Variational Metric Scaling for Metric-Based Meta-Learning
    Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu#, Fu-lai Chung#
    In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), February 2020. (Spotlight Presentation)
    [PDF] [Code]

  • Reconstructing Capsule Networks for Zero-shot Intent Classification
    Han Liu, Xiaotong Zhang, Lu Fan, Xuandi Fu, Qimai Li, Xiao-Ming Wu#, Albert Y.S. Lam
    In Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Long Paper), November 2019.
    [PDF] [ Code ]

  • Attributed Graph Clustering via Adaptive Graph Convolution
    Xiaotong Zhang*, Han Liu*, Qimai Li*, Xiao-Ming Wu#
    In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), August 2019.
    [PDF] [Code]

  • Label Efficient Semi-Supervised Learning via Graph Filtering
    Qimai Li, Xiao-Ming Wu#, Han Liu, Xiaotong Zhang, Zhichao Guan
    In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
    [PDF] [Code]

  • Large Margin Meta-Learning for Few-Shot Classification
    Yong Wang, Xiao-Ming Wu#, Qimai Li, Jiatao Gu, Wangmeng Xiang, Lei Zhang, Victor O.K.Li#
    In Thirty-second Annual Conference on Neural Information Processing Systems Workshop ( NeurIPSW) on Meta-Learning, December 2018.
    [Workshop version] [Early long version on arXiv] [Code]

  • Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
    Qimai Li, Zhichao Han, Xiao-Ming Wu#
    In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), February 2018. (Oral Presentation)
    (Rated as one of the most influential AAAI-2018 papers by PaperDigest)
    [PDF] [Project] [Code]

  • Chapter 14: Partially Absorbing Random Walks: A Unified Framework for Learning on Graphs
    Xiao-Ming Wu#, Zhenguo Li, and Shih-Fu Chang.
    Book Chapter in Cooperative and Graph Signal Processing -- Principles and Applications, Elsevier, June 2018.

  • New Insights into Laplacain Similarity Search
    Xiao-Ming Wu, Zhenguo Li, and Shih-Fu Chang.
    In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
    [PDF] [Supplemental] [Abstract] [Code] [Poster]

  • Locally Linear Hashing for Extracting Non-Linear Manifolds
    Go Irie, Zhenguo Li, Xiao-Ming Wu, and Shih-Fu Chang.
    In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
    [PDF] [Supplemental] [Code] [Poster]

  • Analyzing the Harmonic Structure in Graph-Based Learning
    Xiao-Ming Wu, Zhenguo Li, and Shih-Fu Chang.
    In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), December 2013.
    [PDF] [Supplemental] [Code] [Poster]

  • Learning with Partially Absorbing Random Walks
    Xiao-Ming Wu, Zhenguo Li, Anthony Man-Cho So, John Wright, and Shih-Fu Chang.
    In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), December 2012.
    [PDF] [Supplemental] [Code] [Poster]

  • Segmentation Using Superpixels: A Bipartite Graph Partitioning Approach
    Zhenguo Li, Xiao-Ming Wu, and Shih-Fu Chang.
    In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2012.
    [PDF] [Code] [Project][Poster]

  • Fast Graph Laplacian Regularized Kernel Learning via Semidefinite-Quadratic-Linear Programming.
    Xiao-Ming Wu, Anthony Man-Cho So, Zhenguo Li, and Shuo-Yen Robert Li.
    In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), December 2009. (Spotlight Presentation)
    [PDF] [Code] [Poster]

My Group

I am lucky to work with brilliant students who are willing to follow my research interests, and tackle research problems with me.

Teaching Subjects

The courses I have taught and will teach:

Contact

The best way to reach me is by email. Due to the large volume of emails recieved, I could not respond to every enquiry, but I do read every email. If I do not respond to your inquiry about PhD/MPhil/RA position, it does not necessarily mean you are not suitable for it but may be simply that it is not available.