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huitangtang/README.md

Hi there πŸ‘‹

πŸ‘‹ About Me

I am a Postdoctoral Research Fellow in the Department of Electronic and Computer Engineering at The Hong Kong University of Science and Technology, a core member in Prof. Xiaomeng Li's Lab. I received my Ph.D. degree in the Geometric Perception and Intelligence Research Lab (Gorilla Lab) at South China University of Technology, advised by Prof. Kui Jia. Before that, I received my B.E. degree majoring in Information Engineering at South China University of Technology.

My research interests include deep learning, pattern recognition, computer vision, medical image analysis, and healthcare prediction. I am particularly focused on advancing methodologies and techniques in transfer learning, domain adaptation, semi-supervised learning, clustering, data synthesis, large models, etc.

I actively serve as a reviewer for numerous prestigious conferences and journals, including CVPR, ICCV, AAAI, NeurIPS, ICML, ICLR, ICME, TIP, TNNLS, JBHI, ESWA, PR, NEUNET, NEUCOM, and TMLR, among others.

πŸ“Ž Homepages

πŸ”₯ News

  • 2024.11: My google scholar citations have exceeded 1300. πŸŽ‰
  • 2024.02: A conference paper is accepted by CVPR in 2024. πŸŽ‰
  • 2023.09: I have been a Postdoc in Dept. ECE at HKUST. πŸŽ‰
  • 2023.07: I have received my PhD degree. πŸŽ‰
  • 2023.06: I pass the PhD thesis defence and my thesis is appraised as excellent. πŸŽ‰
  • 2023.05: A new synthetic-to-real benchmark S2RDA is published by the CVF repository. πŸŽ‰
  • 2023.02: A conference paper is accepted by CVPR in 2023. πŸŽ‰
  • 2022.10: A conference paper is published by ECCV in 2022. πŸŽ‰
  • 2022.10: A journal paper is published by TPAMI in 2022. πŸŽ‰
  • 2022.06: A conference paper is published by CVPR in 2022. πŸŽ‰

Popular repositories Loading

  1. On_the_Utility_of_Synthetic_Data On_the_Utility_of_Synthetic_Data Public

    Code release for "A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation", accepted by CVPR2023.

    Python 11 1

  2. DisClusterDA DisClusterDA Public

    Code release for Unsupervised Domain Adaptation via Distilled Discriminative Clustering published by Pattern Recognition in 2022

    Python 10 2

  3. GSF-PPF GSF-PPF Public

    Code release for ``Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning'' published in CVPR 2022.

    Python 8 1

  4. H-SRDC H-SRDC Public

    Code release for Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering (TPAMI 2022).

    Python 7 2

  5. STOCO STOCO Public

    Code release for ``Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers'' accepted by ECCV 2022.

    Python 5 1

  6. ViCatDA ViCatDA Public

    Code release for Vicinal and categorical domain adaptation published by Pattern Recognition in 2021

    Python 3 2