Mr Hai-Ming Xu

Mr Hai-Ming Xu

Postdoc Researcher

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.

My current research interest lies in multi-modal large language models and agents. I have previously researched Semi-supervised Learning, Segmentation, and Kernel-based Learning.

  • Journals
    Year Citation
    2020 Xue, H., Song, Y., & Xu, H. M. (2020). Multiple indefinite kernel learning for feature selection. Knowledge-Based Systems, 191, 1-12.
    DOI Scopus8
    2019 Xue, H., Xu, H., Chen, X., & Wang, Y. (2019). A primal perspective for indefinite kernel SVM problem. Frontiers of Computer Science, 14(2), 349-363.
    DOI Scopus9

  • Conference Papers
    Year Citation
    2024 Tang, P., Xu, H. M., & Ma, C. (2024). ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3314-3324). Online: IEEE.
    DOI Scopus1
    2024 Chang, J., Wang, S., Xu, H. M., Chen, Z., Yang, C., & Zhao, F. (2024). DETRDistill: A Universal Knowledge Distillation Framework for DETR-families. In Proceedings of the IEEE International Conference on Computer Vision Vol. 30 (pp. 6875-6885). Online: IEEE.
    DOI Scopus4
    2024 Zhou, Z., Xu, H. -M., Shu, Y., & Liu, L. (2024). Unlocking the Potential of Pre-Trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Vol. 33 (pp. 3817-3827). Seattle, WA, USA: IEEE.
    DOI
    2023 Wang, S., Zhao, X., Xu, H. M., Chen, Z., Yu, D., Chang, J., . . . Zhao, F. (2023). Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 13333-13342). Online: IEEE COMPUTER SOC.
    DOI Scopus5
    2023 Xu, H. M., Liu, L., Chen, H., Abbasnejad, E., & Felix, R. (2023). Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models. In Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 Vol. 9 (pp. 3284-3294). Online: IEEE.
    DOI
    2022 Xu, H. -M., Liu, L., Bian, Q., & Yang, Z. (2022). Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022) (pp. 14 pages). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).
    2022 Xu, H. M., Liu, L., & Abbasnejad, E. (2022). Progressive Class Semantic Matching for Semi-supervised Text Classification. In M. Carpuat, M. -C. De Marneffe, & I. V. Meza Ruiz (Eds.), NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 3003-3013). Seattle, Washington & Online: Association for Computational Linguistics.
    DOI Scopus9
    2022 Xu, H. M., Chen, H., Liu, L., & Yin, Y. (2022). Dual Decision Improves Open-Set Panoptic Segmentation. In BMVC 2022 - 33rd British Machine Vision Conference Proceedings (pp. 1-13). London, UK: The British Machine Vision Association..
    Scopus3
    2022 Xu, H. M., Liu, L., Bian, Q., & Yang, Z. (2022). Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, & A. Oh (Eds.), Advances in Neural Information Processing Systems Vol. 35 (pp. 1-18). New Orleans, LA, USA: Curran Associates.
    Scopus33
    2022 Shu, Y., Yu, B., Xu, H., & Liu, L. (2022). Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-boosting Attention Mechanism. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13685 LNCS (pp. 449-465). Online: Springer Nature Switzerland.
    DOI Scopus15 WoS4
    2021 Xu, H. M., Liu, L., & Gong, D. (2021). Semi-supervised Learning via Conditional Rotation Angle Estimation. In DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). online: IEEE.
    DOI Scopus4
    2017 Xu, H. -M. (2017). Solving Indefinite Kernel Support Vector Machine with Difference of Convex Functions Programming. In Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence (pp. 1-7). San Francisco, California, USA: Association for the Advancement of Artificial Intelligence.
    Scopus31 WoS18
    2017 Xue, H., Song, Y., & Xu, H. M. (2017). Multiple indefinite kernel learning for feature selection. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Vol. 0 (pp. 3210-3216). Melbourne, Aust: AAAI Press.
    DOI Scopus10 WoS7
  • Position: Postdoc Researcher
  • Email: hai-ming.xu@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor 2
  • Org Unit: Research Scholars (Positions)
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