Wenxuan Tu
Ph.D. PRMI Group, School of Computing, National University of Defense Technology Email: wenxuantu@163.com Address: Sanyi Road, Kaifu District, Changsha, Hunan, China [Google Scholar] [DBLP] [ResearchGate] [Github] [ORCID] |
Short Bio
I received the Ph.D. degree in Computer Science from National University of Defense Technology, under the co-supervision of Professor Zhiping Cai and Professor Xinwang Liu. I have published papers in highly regarded journals and conferences such as IEEE TIP, IEEE TNNLS, IEEE TKDE, IEEE TGRS, IEEE TSMC, AAAI, IJCAI, CVPR, NeurIPS, ICLR, ICML, etc. My research interests include clustering analysis, graph machine learning, and computer vision.
Education Experience
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[Sep. 2020 - Dec. 2023] Doctor, National University of Defense Technology, Changsha, China
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[Sep. 2017 - Jul. 2020] Master, Hunan University, Changsha, China
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[Sep. 2013 - Jul. 2017] Bachelor, Hainan University, Haikou, China
Open Source
Publications
2024
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Meng Liu, Ke Liang, Yawei Zhao, Wenxuan Tu, Sihang Zhou, Xinbiao Gan, Xinwang Liu, Kunlun He: Self-Supervised Temporal Graph Learning with Temporal and Structural Intensity Alignment. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024, (to appear) (CCF Rank B, JCR Q1)
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Xin Peng, Jieren Cheng, Xiangyan Tang, Ziqi Deng, Wenxuan Tu, and Neal Xiong. HSNet: An Intelligent Hierarchical Semantic-Aware Network System for Real-Time Semantic Segmentation. IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC), 2024, (to appear). (CCF Rank B, JCR Q1)
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Renxiang Guan, Zihao Li, Wenxuan Tu, Jun Wang, Yue Liu, Xianju Li, Chang Tang, and Ruyi Feng. Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks. IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2024. (CCF Rank B, JCR Q1) [Paper], [Code]
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Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, and Xinwang Liu. Deep Temporal Graph Clustering. Proceedings of the Twelfth International Conference on Learning Representations (ICLR), 2024. (CAA/THU Rank A) [Paper], [Code]
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Wenxuan Tu, Bin Xiao, Xinwang Liu, Sihang Zhou, Zhiping Cai, and Jieren Cheng. Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024. (CCF Rank B, JCR Q1) [Paper], [Code]
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Wenxuan Tu, Renxiang Guan, Sihang Zhou, Chuan Ma, Xin Peng, Zhiping Cai, Zhe Liu, Jieren Cheng, and Xinwang Liu. Attribute-Missing Graph Clustering Network. Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024, pages: 15392-15401. (CCF Rank A) [Paper], [Code]
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Xin Peng, Jieren Cheng, Xiangyan Tang, Bin Zhang, and Wenxuan Tu. Multi-View Graph Imputation Network. Information Fusion, 102: 102024, 2024. (JCR Q1) [Paper], [Code]
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Shengju Yu, Siwei Wang, Zhibin Dong, Wenxuan Tu, Suyuan Liu, Zhao Lv, Pan Li, Miao Wang, and En Zhu. A Non-Parametric Graph Clustering Framework for Multi-View Data. Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024, pages: 16558-16567. (CCF Rank A) [Paper]
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Ke Liang, Lingyuan Meng, Sihang Zhou, Wenxuan Tu, Siwei Wang, Yue Liu, Meng Liu, Long Zhao, Xiangjun Dong, and Xinwang Liu. MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs. Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024, pages: 10645-10653. (CCF Rank A) [Paper]
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Ke Liang, Sihang Zhou, Meng Liu, Yue Liu, Wenxuan Tu, Yi Zhang, Liming Fang, Zhe Liu, and Xinwang Liu. Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning based on Criminal Correlations. Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024, pages: 8733-8741. (CCF Rank A) [Paper]
2023
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Wenxuan Tu, Qing Liao, Sihang Zhou, Xin Peng, Chuan Ma, Zhe Liu, Xinwang Liu, Zhiping Cai, and Kunlun He. RARE: Robust Masked Graph Autoencoder. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. (CCF Rank A, JCR Q1) [Paper], [Code]
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Meng Liu, Wenxuan Tu, Ke Liang, and Xinwang Liu. Structural Embedding Pre-Training for Deep Temporal Graph Learning. Proceedings of the Fifth China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), 2023. (CAA Rank A, Best Student Paper) [Paper]
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Meng Liu, Ke Liang, Dayu Hu, Hao Yu, Yue Liu, Lingyuan Meng, Wenxuan Tu, Sihang Zhou, and Xinwang Liu. TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification. Proceedings of the Thirty-First ACM International Conference on Multimedia (ACM MM), 2023, pages: 3365–3374. (CCF Rank A) [Paper], [Code]
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Wenxuan Tu, Sihang Zhou, Xinwang Liu, Chunpeng Ge, Zhiping Cai, and Yue Liu. Hierarchically Contrastive Hard Sample Mining for Graph Self-Supervised Pre-Training. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023. (CCF Rank B, JCR Q1) [Paper], [Code]
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Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, and Xinwang Liu. Knowledge Graph Contrastive Learning based on Relation-Symmetrical Structure. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. (CCF Rank A, JCR Q1) [Paper], [Code]
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Dayu Hu, Ke Liang, Sihang Zhou, Wenxuan Tu, Meng Liu, and Xinwang Liu. scDFC: A Deep Fusion Clustering Method for Single-Cell RNA-Seq Data. Briefings in Bioinformatics (BIB), 2023. (CCF Rank B, JCR Q1) [Paper], [Code]
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Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Siwei Wang, Ke Liang, Wenxuan Tu, and Liang Li. Simple Contrastive Graph Clustering. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. (CCF Rank B, JCR Q1) [Paper], [Code]
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Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, and Xinwang Liu. Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning. Proceedings of the Forty-sixth International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023, pages: 1559-1568. (CCF Rank A) [Paper], [Code]
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Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, and En Zhu. Cluster-Guided Contrastive Graph Clustering Network. Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023, pages: 10834-10842. (CCF Rank A) [Paper], [Code]
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Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, and Cancan Chen. Hard Sample Aware Network for Contrastive Deep Graph Clustering. Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023, pages: 8914-8922. (CCF Rank A) [Paper], [Code]
2022
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Lei Gong*, Wenxuan Tu*, Sihang Zhou, Long Zhao, Zhe Liu, and Xinwang Liu. Deep Fusion Clustering Network with Reliable Structure Preservation. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. (CCF Rank B, JCR Q1) [Paper], [Code]
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Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, and En Zhu. Align then Fusion: Generalized Large-Scale Multi-View Clustering with Anchor Matching Correspondences. Proceedings of the Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS), 2022. (CCF Rank A) [Paper], [Code]
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Wenxuan Tu, Sihang Zhou, Xinwang Liu, Yue Liu, Zhiping Cai, En Zhu, Changwang Zhang, and Jieren Cheng. Initializing Then Refining: A Simple Graph Attribute Imputation Network. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI), 2022, pages: 3494-3500. (CCF Rank A) [Paper], [Code]
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Lei Gong, Sihang Zhou, Wenxuan Tu, and Xinwang Liu. Attributed Graph Clustering with Dual Redundancy Reduction. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI), 2022, pages: 3015-3021. (CCF Rank A) [Paper], [Code]
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Siwei Wang, Xinwang Liu, Li Liu, Wenxuan Tu, Xinzhong Zhu, Jiyuan Liu, Sihang Zhou, Xinwang Liu, and En Zhu. Highly-Efficient Incomplete Large-Scale Multi-View Clustering with Consensus Bipartite Graph. Proceedings of the Thirty-Fifth IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pages: 9776-9785. (CCF Rank A) [Paper], [Code]
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Yue Liu*, Wenxuan Tu*, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, and En Zhu. Deep Graph Clustering via Dual Correlation Reduction. Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022, pages: 7603-7611. (CCF Rank A) [Paper], [Code]
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Yuxin Huang, Miaomiao Li, Wenxuan Tu, Jiyuan Liu, and Jiahao Ying. Spare Simple MKKM with Semi-Infinite Linear Program Optimization. International Journal of Intelligent Systems (IJIS), 37(2): 1113-1128, 2022. (CCF Rank C, JCR Q1) [Paper]
2021
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Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En hu, and Jieren Cheng. Deep Fusion Clustering Network. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021, pages: 9978-9987. (CCF Rank A) [Paper], [Code]
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Xiangyan Tang, Wenxuan Tu, Keqiu Li, and Jieren Cheng. DFFNet: An IoT-Perceptive Dual Feature Fusion Network for General Real-Time Semantic Segmentation. Information Sciences, 565: 326-343, 2021. (CCF Rank B, JCR Q1) [Paper], [Code]
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Xinwang Liu, Li Liu, Qing Liao, Chang Tang, Siwei Wang, Wenxuan Tu, Jiyuan Liu, Yi Zhang, and En Zhu. One Pass Late Fusion Multi-View Clustering. Proceedings of the Thirty-Eighth International Conference on Machine Learning (ICML), 2021, pages: 6850-6859. (CCF Rank A) [Paper], [Code]
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Mengjing Sun, Pei Zhang, Siwei Wang, Sihang Zhou, Wenxuan Tu, Xinwang Liu, En Zhu, and Changjian Wang. Scalable Multi-View Subspace Clustering with Unified Anchors. Proceedings of the Twenty-Ninth ACM International Conference on Multimedia (ACM MM), 2021, pages: 3528-3536. (CCF Rank A) [Paper], [Code]
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Jiyuan Liu, Xinwang Liu, Yi Zhang, Pei Zhang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Weixuan Liang, Siqi Wang, and Yuexiang Yang. Self-Representation Subspace Clustering for Incomplete Multi-View Data. Proceedings of the Twenty-Ninth ACM International Conference on Multimedia (ACM MM), 2021, pages: 2726–2734. (CCF Rank A) [Paper], [Code]
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Jieren Cheng, Xin Peng, Xiangyan Tang, Wenxuan Tu, and Wenhang Xu. MIFNet: A Lightweight Multi-Scale Information Fusion Network. International Journal of Intelligent Systems (IJIS), 37(9): 5617-5642, 2022. (CCF Rank C, JCR Q1) [Paper]
2020 & Before
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Bin Jiang, Wenxuan Tu, Chao Yang, and Junsong Yuan. Context-Integrated and Feature-Refined Network for Lightweight Object Parsing. IEEE Transactions on Image Processing (TIP), 29: 5079-5093, 2020. (CCF Rank A, JCR Q1) [Paper], [Code]
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Jiankai He, Bin Jiang, Chao Yang, Wenxuan Tu. Hybrid Dilated Convolution Network Using Attentive Kernels for Real-Time Semantic Segmentation. Pattern Recognition and Computer Vision (PRCV), 2018. (CCF Rank C) [Paper]
Under Review
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Wenxuan Tu, Sihang Zhou, Xinwang Liu, Zhiping Cai, Yawei Zhao, Yue Liu, and Kunlun He. WAGE: Weight-Sharing Attribute-Missing Graph Autoencoder. 2023, (under review).
Awards
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The Honorary Title of "Academic New Star" in the 2023 IJCAI Youth Elite Academic Conference, Shanghai China, 2023.07
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China Aerospace Science and Technology Corporation Scholarship, Changsha China, 2022.10
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Outstanding Student Award of School of Computing, National University of Defense Technology, Changsha China, 2022.09
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Outstanding Thesis Award for Master's Degree of Hunan University, Changsha China, 2022.06
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Outstanding Thesis Award for Master's Degree of Hunan Association for Artificial Intelligence, Changsha China, 2021.10
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Outstanding Student Award of School of Computing, National University of Defense Technology, Changsha China, 2021.07
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Outstanding Graduated Graduate Student of Hunan Province, Changsha China, 2020.06
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Outstanding Graduated Graduate Student of Hunan University, Changsha China, 2020.06
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2018 Future Cup University AI Challenges, 1st Place in Central China & Southern China, Changsha China, 2018.09
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2018 Future Cup University AI Challenges, 3rd Place in National Finals, Beijing China, 2018.06
Services
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Program Committee Member for AAAI 2023-2024, ACM MM 2021-2024, CVPR 2022-2024, ICML 2024, ICLR 2024, ECCV 2024, NeurIPS 2023, ICME 2021-2024, ICASSP 2023-2024, ACCV 2023
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Reviewer for IEEE TPAMI, IEEE TKDE, IEEE TNNLS, ACM TOMM, Neurocomputing
Contact
Any discussions or concerns are welcomed! Email: [wenxuantu@163.com]