Xiaocheng Lu

Xiaocheng Lu

PhD@HKUST, Intern@Shanghai AiLab

👋👋Hi, I’m Xiaocheng Lu. Now, I’m pursuing my Ph.D. in the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology (HKUST, 香港科技大学), supervised by Prof. Song Guo in PEILab.

Before that, I received my Master degree in Artificial Intelligence, Optics, and Electronics (iOPEN), Northwestern Polytechnical University (NWPU, 西北工业大学), supervised by Qi Wang, Yuan Yuan and Xuelong Li professors. Also, I received a Bachelor degree at the School of Automation, Northwestern Polytechnical University (NWPU， 西北工业大学). During this period, my research areas included computer vision, image/video enhancement learning, scene text recognition, etc.

Recently, my research interests include Vision/Large Language Models, Zero/Few-shot Learning, AI for Science, and Continuous Learning. If you are interested in these topics, please feel free to e-mail me. Thanks a lot!

🔥 News

  • [Action] 2023.10: Now I move to Shanghai AiLab as an intern for Ai4Earth.
  • [Paper] 2023.02: 🎉🎉 Two papers are accepted by CVPR 2023!
  • [Code] 2023.02: 👨🏻‍💻A repo for the CZSL paper list has been released; welcome to follow. Link
  • [Paper] 2022.12: 🎉🎉 One paper is accepted by AAAI 2023!
  • [Code] 2022.11: 👨🏻‍💻 The code for DFSP has been released!
  • [Paper] 2022.02: 🎉🎉 One paper is accepted by T-PAMI!
  • [Paper] 2021.05: 🎉🎉 One paper is accepted by ICMR 2021!

📝 Publications

📄 Preprints

  • arXiv 2023 J. Guo, Q. Zhou, R. Li, X. Lu, Z. Liu, J. Chen, X. Xie, J. Zhang, “ParsNets: A Parsimonious Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning,” arXiv:2312.09709, 2023. [PDF]

  • arXiv 2023 Z. Rao, J. Guo, X. Lu, Qihua Zhou, J. Zhang, K. Wei, C. Li, S. Guo, “Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning,” arXiv:2311.14750, 2023. [PDF]

  • arXiv 2023 Z. Liu, J. Guo, X. Lu, S. Guo, P. Dong, J. Zhang, “GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning,” arXiv:2309.00923, 2023. [PDF]

  • arXiv 2023 X. Lu, Z. Liu, S. Guo, J. Guo, F. Huo, S. Bai, T. Han, “DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning,” arXiv:2305.01239, 2023. [PDF][Code]

🎙 Conferences

  • IJCAI 2024 Z. Rao, J. Guo, X. Lu, J. Liang, J. Zhang, H. Wang, K. Wei, X. Cao, “Dual Expert Distillation Network for Generalized Zero-Shot Learning,,” International Joint Conference on Artificial Intelligence (IJCAI, CCF-A), 2024.

  • IJCAI 2024 J. Guo, Q. Zhou, X. Lu, R. Li, Z. Liu, J. Zhang, B. Han, J. Chen, X. Xie, S. Guo, “ParsNets: A Parsimonious Composition of Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning,” International Joint Conference on Artificial Intelligence (IJCAI, CCF-A), 2024.

  • CVPR 2024 S. Bai, J. Zhang, S. Guo, S. Li, J. Guo, J. Hou, T. Han, and X. Lu, “DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning,” Proceedings of Computer Vision and Pattern Recognition (CVPR, CCF-A), 2024.

  • AAAI 2024 F. Huo, W. Xu, S. Guo, J. Guo, H. Wang, Z. Liu, X. Lu, “ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning,” Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI, CCF-A), 2024.

  • CVPR 2023 X. Lu, S. Guo, Z. Liu, J. Guo, “Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning,” Proceedings of Computer Vision and Pattern Recognition (CVPR, CCF-A), 2023. [PDF][Code]

  • CVPR 2023 Z. Liu, S. Guo, X. Lu, J. Guo, J. Zhang, Y. Zeng, F. Huo, “$(ML)^2$ P-Encoder: On Exploration of Channel-class Correlation for Multi-label Zero-shot Learning,” Proceedings of Computer Vision and Pattern Recognition (CVPR, CCF-A), 2023.[PDF][Code]

  • AAAI 2023 J. Guo, S. Guo, Q. Zhou, Z. Liu, X. Lu, F. Huo, “Graph knows unknowns: Reformulate zero-shot learning as sample-level graph recognition,” Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI, CCF-A), 2023.[PDF]

  • ICMR 2021 X. Lu, Y. Yuan, and Q. Wang, “AWFA-LPD: Adaptive Weight Feature Aggregation for Multi-frame License Plate Detection,” Proc. ACM International Conference on Multimedia Retrieval (ICMR, CCF-B), pp. 476-480, 2021.[PDF]

📚 Journals

  • T-PAMI Q. Wang, X. Lu, C. Zhang, Y. Yuan, and X. Li, “LSV-LP: Large-Scale Video-Based License Plate Detection and Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI, IF=24.3134, CCF-A, JCR-Q1), DOI: 10.1109/TPAMI.2022.3153691. [PDF][Dataset]

🎖 Honors and Awards

  • 2022.03, Outstanding Graduate Student (Top 10%)
  • 2021.09, Campus Job Offers: Bytedance, Baidu, Huawei, etc.
  • 2020.01, Huawei Cloud Artificial Intelligence Innovation Application Competition (Top 2%)
  • 2017.08, National University Student NXP Cup Smart Car Competition National Finals Grand Prize (1st in 300+ teams)

📖 Educations

🔍 Regular Reviewer

  • CVPR, ACMMM, AAAI, ICLR
  • TPAMI, TSCVT

🛠 Skills

  • English: IELTS 6.5
  • Programming Skills: Python, C/C++, Golang, Javascript, Pytorch, Tensorflow
  • Computer Basics: Computer Network, Operating System, Database (Mysql, Redis)
  • Typesetting & Graphing: TEX and friends(e.g., LATEX, Highcharts)
  • Other skills: Docker, Linux, Distributed Systems, Multi-threaded Programming, K8S, etc.

💻 Project

  • 2021.09 - 2021.12, Knowledge Graph Construction for Aero-engine, Wuxi.
  • 2020.04 - 2021.06, Knowledge Graph for Battlefield Situation Assessment, Tsinghua.

🔬 Internships

  • 2023.10 - Present, Shanghai AiLab, Shanghai.
  • 2021.05 - 2021.08, Tencent, Beijing.

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