Correspondence

Mail: School of Computer Science and Engineering,
Jiulonghu Campus, Southeast University,
Nanjing 211189, China
Office: 512, Computer Science Building,
Jiulonghu Campus, Southeast University
Nanjing 211189, China
Email: hxue@seu.edu.cn

Brief Introduction | Recruitment | News | Selected Publications | Courses | Codes & Data

Brief Introduction

My research interests mainly include machine learning and pattern recognition. Currently, I am a professor at the PALM Group, School of Computer Science and Engineering, Southeast University. I received my B.Sc. degree in mathematics from Nanjing Normal University in 2002, and M.Sc. degree in mathematics from Nanjing University of Aeronautics & Astronautics (NUAA) in 2005. In 2008, I received my Ph.D. degree in computer science also from NUAA. I was also a member of PARNEC group led by my supervisor Prof. Songcan Chen.
Research Interest

Recruitment

Looking for highly self-motivated undergraduate students aiming to enroll in a MSc. program.
欢迎2024级考研生与我邮件联系. （目前仍有部分2024级硕士生名额）

News

[2024.04.02] One paper is accepted by TNNLS.
[2023.12.09] One paper is accepted by AAAI`24.
[2023.09.22] One paper is accepted by NeurIPS`23.
[2023.04.20] Three papers are accepted by IJCAI`23.
[2023.02.05] One paper is accepted by TPAMI.

Selected Publications

  * denotes corresponding author.

Conference Papers


  1. Shipeng Zhu, Pengfei Fang, Chenjie Zhu, Zuoyan Zhao, Qiang Xu, Hui Xue*. Text Image Inpainting via Global Structure-Guided Diffusion Models. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 38(7): 7775-7783, Vancouver, Canada, 2024. [PDF]

  2. Yangfang Xue, Pengfei Fang, Jinyue Tian, Shipeng Zhu, Hui Xue*. CosNet: A Generalized Spectral Kernel Network. In: Advances in Neural Information Processing Systems (NeurIPS'23), 36: 37273-37285, New Orleans, USA, 2023. [PDF]

  3. Meimei Yang, Pengfei Fang, Hui Xue*. Expanding the Hyperbolic Kernels: A Curvature-aware Isometric Embedding View. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), 4469-4477, Macao, China, 2023. [PDF]

  4. Yuexuan An, Xingyu Zhao, Hui Xue*. Learning to Learn from Corrupted Data for Few-Shot Learning. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), 3423-3431, Macao, China, 2023. [PDF]

  5. Yongjuan Che, Yuexuan An, Hui Xue*. Boosting Few-Shot Open-Set Recognition with Multi-Relation Margin Loss. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), 3505-3513, Macao, China, 2023. [PDF]

  6. Shipeng Zhu, Zuoyan Zhao, Pengfei Fang, Hui Xue*. Improving Scene Text Image Super-resolution via Dual Prior Modulation Network. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 37(3): 3843-3851, Washington DC, USA, 2023. [PDF]

  7. Zheng-Fan Wu, Yi-Nan Feng, Hui Xue*. Automatically Gating Multi-Frequency Patterns through Rectified Continuous Bernoulli Units with Theoretical Principles. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), 3594-3600, Vienna, Austria, 2022. [PDF]

  8. Yuexuan An, Hui Xue*, Xingyu Zhao, Lu Zhang. Conditional Self-Supervised Learning for Few-Shot Classification. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), 2140-2146, Montreal, Quebec, Canada, 2021. [PDF]

  9. Zheng-Fan Wu, Hui Xue*, Weimin Bai. Learning Deeper Non-monotonic Networks by Softly Transferring Solution Space. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), 3200-3206, Montreal, Quebec, Canada, 2021. [PDF]

  10. Qiao Liu, Hui Xue*. Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaption. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), 2744-2750, Montreal, Quebec, Canada, 2021. [PDF]

  11. Hui Xue*, Zheng-Fan Wu. BaKer-Nets: Bayesian Random Kernel Mapping Networks. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), 3073-3079, Yokohama, Japan, 2020. [PDF]

  12. Hui Xue*, Xin-Kai Sun, Wei-Xiang Sun. Multi-hop Hierarchical Graph Neural Networks. In: Proceedings of the 7th IEEE International Conference on Big Data and Smart Computing (BigComp'20), 82-89, 2020. [PDF]

  13. Hui Xue*, Zheng-Fan Wu, Wei-Xiang Sun. Deep Spectral Kernel Learning. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), 4019-4025, Macao, China, 2019. [PDF]

  14. Fa Zheng, Hui Xue*. Subclass Maximum Margin Tree Error Correcting Output Codes. In: Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI'18), LNAI 11012, 454-462, Nanjing, China, 2018. [PDF]

  15. Hui Xue*, Yu Song, Hai-Ming Xu. Multiple Indefinite Kernel Learning for Feature Selection. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 3210-3216, Melbourne, Australia, 2017. [PDF]

  16. Hai-Ming Xu, Hui Xue*, Xiao-Hong Chen, Yun-Yun Wang. Solving Indefinite Kernel Support Vector Machine with Difference of Convex Functions Programming. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17),2782-2788, San Francisco, California, USA, 2017. [PDF]

  17. Fa Zheng, Hui Xue*, Xiaohong Chen, Yunyun Wang. Maximum Margin Tree Error Correcting Output Codes. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI'16), LNAI 9810, 681-691, Phuket, Thailand, 2016. [PDF]

  18. Chao Xing, Xin Geng, Hui Xue. Logistic Boosting Regression for Label Distribution Learning. In: Proceedings of the 26th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'16), 4489-4497, Las Vegas, Nevada, USA, 2016. [PDF]

  19. Yin Zhou, Hui Xue, Xin Geng. Emotion Distribution Recognition from Facial Expressions. In: Proceedings of the 23rd ACM International Conference on Multimedia (ACM MM'15), 1247-1250, Brisbane, Australia, 2015. [PDF]

  20. Dayin Zhang, Hui Xue*. Lp-norm Multiple Kernel Learning with Diversity of Classes. In: Proceedings of the 13rd Pacific Rim Knowledge Acquisition Workshop (PKAW'14), 38-47, Gold Coast, Australia, 2014. [PDF]

  21. Hui Xue*, Songcan Chen, Jijian Huang. Discriminative Indefinite Kernel Classifier from Pairwise Constraints and Unlabeled Data. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR'12), 497-500, Tsukuba, Japan, 2012. [PDF]

  22. Jijian Huang, Hui Xue*, Yuqing Zhai. Semi-supervised Discriminatively Regularized Classifier with Pairwise Constraints. In: Proceedings of the 12th Pacific Rim International Conference on Artificial Intelligence (PRICAI'12), LNAI 7458, 112-123, Kuching, Sarawak, Malaysia, 2012. [PDF]

  23. Hui Xue, Songcan Chen, Qiang Yang. Structural Support Vector Machine. In: Proceedings of the 15th International Symposium on Neural Networks (ISNN'08), Part I, LNCS5263, 501-511, 2008. [PDF]

  24. Hui Xue, Songcan Chen. Alternative Robust Local Embedding. In: Proceedings of the 1st International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR'07), 591-596, 2007. [PDF]

Journal Papers


  1. Yuexuan An, Hui Xue*, Xingyu Zhao, Ning Xu, Pengfei Fang, Xin Geng. Leveraging Bilateral Correlations for Multi-Label Few-Shot Learning. IEEE Transactions on Neural Networks and Learning Systems (TNNLS'24), in press, 2024.

  2. Yuexuan An, Hui Xue*, Xingyu Zhao, Jing Wang. From Instance to Metric Calibration: A Unified Framework for Open-World Few-Shot Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI'23), 45(8): 9757-9773, 2023. [PDF]

  3. Meimei Yang, Qiao Liu, Xinkai Sun, Na Shi, Hui Xue*. Towards Kernelizing the Classifier for Hyperbolic Data. Frontiers of Computer Science (FCS'24), 18(1), 2024.

  4. Qipeng Zhu, Zhuoyan Luo, Shipeng Zhu, Qi Jing, Zihang Xu, Hui Xue*. FATE: A Three-stage Method for Arithmetical Exercise Correction. Neural Computing and Applications (NCA'23), 35: 23491-23506, 2023. [PDF]

  5. Jing Wang, Xin Geng, Hui Xue. Re-weighting Large Margin Label Distribution Learning for Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI'22), 44(9): 5445-5459, 2022. [PDF]

  6. Yuexuan An, Hui Xue*. Indefinite Twin Support Vector Machine with DC Functions Programming. Pattern Recognition (PR'22), 121, 2022. [PDF]

  7. Qing Tian, Chuang Ma, Fengyuan Zhang, Shun Peng, Hui Xue. Source-free Unsupervised Domain Adaption with Sample Transport Learning. Journal of Computer Science and Technology (JCST'21), 36(3): 606-616, 2021. [PDF]

  8. Weixiang Sun, Hui Xue*. Learning Graph-level Representation from Local-structural Distribution with Graph Neural Networks. Knowledge-Based Systems (KBS'21), 230, 2021. [PDF]

  9. Jun Jiao, Hui Xue*, Jundi Ding. Non-local Duplicate Pooling Network for Salient Object Detection. Applied Intelligence (Appl Intell'21), 51: 6881-6894, 2021. [PDF]

  10. Hui Xue*, Yu Song, Haiming Xu. Multiple Indefinite Kernel Learning for Feature Selection. Knowledge-Based Systems (KBS'20), 191(5), 2020. [PDF]

  11. Hui Xue*, Zhen Ren. Sketch Discriminatively Regularized Online Gradient Descent Classification. Applied Intelligence (Appl Intell'20), 50: 1367-1378, 2020. [PDF]

  12. Hui Xue*, Yu Song. Non-convex Approximation based l0-norm Multiple Indefinite Kernel Feature Selection. Applied Intelligence (Appl Intell'20), 50: 192-202, 2020. [PDF]

  13. Hui Xue*, Haiming Xu, Xiaohong Chen, Yunyun Wang. A Primal Perspective for Indefinite Kernel SVM Problem. Frontiers of Computer Science (FCS'20), 14(2): 349-363, 2020. [PDF]

  14. Hui Xue*, Lin Wang, Songcan Chen, Yunyun Wang. A Primal Framework for Indefinite Kernel Learning. Neural Processing Letters (NPL'19), 50: 165-188, 2019. [PDF]

  15. Hui Xue*, Sen Li, Xiaohong Chen, Yunyun Wang. A Maximum Margin Clustering Algorithm based on Indefinite Kernels. Frontiers of Computer Science (FCS'19), 13(4), 813-827, 2019. [PDF]

  16. Yunyun Wang, Yan Meng, Yun Li, Songcan Chen, Zhenyong Fu, Hui Xue*. Semi-superivsed Manifold Regularization with Adaptive Graph Construction. Pattern Recognition Letters (PRL'17), 98(15), 90-95, 2017. [PDF]

  17. Yunyun Wang, Yan Meng, Zhengyong Fu, Hui Xue. Towards Safe Semi-supervised Classification: Adjusted Cluster Assumption via Clustering. Neural Processing Letters (NPL'17), 46(3): 1031-1042, 2017. [PDF]

  18. Qing Tian, Hui Xue, Lishan Qiao. Human Age Estimation by Considering both the Ordinality and Similarity of Ages. Neural Processing Letters (NPL'16), 43: 505-521, 2016. [PDF]

  19. Mingxia Liu, Daoqiang Zhang, Songcan Chen, Hui Xue. Joint Binary Classifier Learning for ECOC-based Multi-class Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI'16), 38(11): 2335-2341, 2016. [PDF]

  20. Yunyun Wang, Songcan Chen, Hui Xue* , Zhenyong Fu. Semi-supervised Classification Learning by Discrimination-aware Manifold Regularization. Neurocomputing (Neurocomp'15), 147: 299-306, 2015. [PDF]

  21. Jun Xie, Lu Yu, Lei Zhu, Hui Xue . Boosting Decision Stumps to Do Pairwise Classification. Electronics Letters (EL'14), 50(12): 866-867, 2014. [PDF]

  22. Hui Xue*, Songcan Chen. Discriminality-driven Regularization Framework for Indefinite Kernel Machine. Neurocomputing (Neurocomp'14), 133: 209-221, 2014. [PDF]

  23. Hui Xue*, Songcan Chen. Orthogonality-based Label Correction in Multi-class Classification . Electronics Letters (EL'13), 49(12): 754-755, 2013. [PDF]

  24. Xiaohong Chen, Songcan Chen, Hui Xue. Universum Linear Discriminant Analysis. Electronics Letters (EL'12), 48(22): 1407-1408, 2012. [PDF]

  25. Yunyun Wang, Songcan Chen, Hui Xue. Can Under-exploited Structure of Original-classes Help ECOC-based Multi-class Classification? Neurocomputing (Neurocomp'12), 89: 158-167, 2012. [PDF]

  26. Xiaohong Chen, Songcan Chen, Hui Xue, Xudong Zhou. A Unified Dimensionality Reduction Framework for semi-paired and semi-supervised multi-view data. Pattern Recognition (PR'12), 45(5): 2005-2018, 2012. [PDF]

  27. Xiaohong Chen, Songcan Chen, Hui Xue. Large Correlation Analysis. Applied Mathematics and Computation (AMC'11). 217(22): 9041-9052, 2011. [PDF]

  28. Hui Xue, Songcan Chen, Qiang Yang. Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier. IEEE Transactions on Neural Networks (TNN'11), 22(4): 573-587, 2011. [PDF]

  29. Hui Xue, Songcan Chen. Glocalization Pursuit Support Vector Machine. Neural Computing and Applications (NCA'11), 20(7): 1043-1053, 2011. [PDF]

  30. Yunyun Wang, Songcan Chen, Hui Xue. Support Vector Machines Incorporated with Feature Discrimination. Expert Systems with Applications (ESA'11), 38: 12506-12513, 2011. [PDF]

  31. Yunyun Wang, Songcan Chen, Hui Xue. Structure-embedded AUC-SVM. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI'10), 24(5): 667-690, 2010. [PDF]

  32. Zhe Wang, Songcan Chen, Hui Xue, Zhisong Pan. A Novel Regularization Learning for Single-view Patterns: Multi-view Discriminative Regularization. Neural Processing Letters (NPL'10), 31(3): 159-175, 2010. [PDF]

  33. Hui Xue, Songcan Chen, Qiang Yang. Discriminatively Regularized Least-squares Classification. Pattern Recognition (PR'09), 42(1): 93-104, 2009. [PDF]

  34. Hui Xue, Yulian Zhu, Songcan Chen. Local Ridge Regression for Face Recognition. Neurocomputing (Neurocomp'09), 72: 1342-1346, 2009. [PDF]

  35. Hui Xue, Songcan Chen, Qiang Yang. Discriminative Regularization: A New Classifier Learning Method. Transactions of Nanjing University of Aeronautics & Astronautics (TNUAA'09), 26(1): 65-74, 2009. [PDF]

  36. Hui Xue, Qiang Yang, Songcan Chen. Support Vector Machines. The Top Ten Algorithms in Data Mining, 37-59, Chapman & Hall/CRC, Data Mining and Knowledge Discovery Series, Taylor and Francis Group, 2009. [PDF]

  37. Hui Xue, Songcan Chen, Xiaoqin Zeng. Classifier Learning with A New Locality Regularization Method. Pattern Recognition (PR'08), 41(5): 1479-1490, 2008. [PDF]

Courses

1. Discrete Mathematics [打包下载]

Time: Spring Semester
To: Undergraduate students
Book: 屈婉玲, 耿素云, 张立昂. 离散数学, 高等教育出版社, 2008
Slides: Ch1 Ch2 Ch3 Ch4 Ch5 Ch6 Ch7 Ch8 Ch9 Ch10 Ch11 Ch12 Ch13 Ch14 Ch15 Ch16

2. Pattern Recognition [打包下载]

Time: Autumn Semester
To: Graduate students
Book: Richard O.Duda, Peter E.Hart, David G.Stork. Pattern Classification 2nd Edition, John Wiley & sons, 2004
Slides: Ch1 Ch2 Ch3 Ch4 Ch5 Ch6 Ch7 Ch8 Ch9 Ch10

Codes & Data