Zheng Zhang
Associate Professor
Harbin Institute of Technology, Shenzhen

Dr. Zheng Zhang is a faculty member at School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China, and also holds an adjunct position at Peng Cheng Laboratory, Shenzhen, China. He is the deputy director of the Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, China.

Openings: I am continuously looking for highly-motivated Ph.D. students and postdoctoral researchers to work on machine learning, computer vision and multimedia. Please send me your CV if interested. I can supervise Ph.D. students affiliated with Harbin Institute of Technology as well as Peng Cheng Laboratory. You also may refer to my current research group for detailed information.

"Happiness depends upon ourselves." — Aristotle
"Everything should be as simple as possible, but not simpler." — Albert Einstein
"Not everything that counts can be counted, and not everything that’s counted truly counts." — Albert Einstein


Dr. Zheng Zhang received his Ph.D. degree in Computer Applied Technology from the Harbin Institute of Technology (HIT), advised by Prof. Yong Xu. He visited the National Laboratory of Pattern Recognition (NLPR) at Institute of Automation of Chinese Academy of Sciences (CAS), Beijing, working with Prof. Cheng-Lin Liu (Director of the Laboratory, IEEE/IAPR Fellow), from Jun. 2015 to Jun. 2016. After obtaining his doctoral degree, he became an Assistant Researcher at The Hong Kong Polytechnic University, working with Prof. Calvin Wong, from Apr. 2018 to Oct. 2018, and later was a Postdoctoral Research Fellow at Data Science Group, School of Information Technology and Electrical Engineering, The University of Queensland, Australia, supervised by Prof. Helen Huang, from Oct. 2018 to Oct. 2019. During his Ph.D. study and academic career, he was fortunately mentored by Prof. Heng Tao Shen (Member of Academia Europaea, ACM/IEEE/OSA Fellow) and Prof. Ling Shao (IEEE/IAPR Fellow). Since 2019, he has been with School of Computer Science & Technology, Harbin Institute of Technology, Shenzhen, China, where he currently serves as the deputy director of the Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, China.

Dr. Zhang's research interests mainly focus on multimedia content analysis and understanding, especially multimedia retrieval, multi-modal learning, and big data mining. He has published more than 100 technical papers at prestigious international journals and conference proceedings. He is a co-recipient of paper awards in ACM Multimedia Asia'21, EAI ICMTEL'22 and SMARTCOMP'14. He is currently at the Editorial Board of IEEE Trans. on Affective Computing (IEEE TAC), IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), and Elsevier Information Fusion (INFFUS). His Ph.D. thesis was awarded the Distinguished Ph.D. Dissertation of The Chinese Institute of Electronics, Outstanding Ph.D. Dissertation finalist of The Chinese Association for Artificial Intelligence (CAAI) as well as Distinguished Doctoral Degree Thesis Award of HIT. He was the recipient of the CAAI Outstanding Young Research Achievement Award and also has been featured in the 'World's Top 2% Scientists List' for consecutive years. He is an IEEE and CCF Senior Member.

Research Interests

What's New

Selected Publications (New Homepage)
  1. Zheng Zhang, Yong Xu, Guangming Lu, Structural Representation Learning for Data Analysis, Posts & Telecom Press, China, ISBN:978-7-115-58401-4, 2022. (Sponsored by the National Publishing Foundation of China, 2022.)
    张正,徐勇,卢光明,数据分析的结构化表征学习,人民邮电出版社,ISBN:978-7-115-58401-4,2022. (国家出版基金项目和“十四五”时期国家重点出版物出版专项规划项目联合支持)
  2. Lei Zhu, Jingjing Li, Zheng Zhang, Dynamic Graph Learning for Dimension Reduction and Data Clustering, Synthesis Lectures on Computer Science (SLCS), ISBN: 978-3-031-42312-3, Springer Nature, 2023.
  3. Xiaochun Yang, Chang-Dong Wang, Saiful Islam, Zheng Zhang (Eds.), The 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, Foshan, China, Nov. 12-14 2020, Springer LNAI, vol. 12447, ISBN 978-3-030-65389-7, 2020.
  4. Shuihua Wang, Zheng Zhang, Yuan Xu (Eds.), The IoT and Big Data Technologies for Health Care, The second EAI International Conference, IoTCARE 2021, October 18-19, 2021, Springer LNICS, Social Informatics and Telecommunications Engineering, 2021.

Selected Publication:
  1. Z. Zhang, L. Liu, F. Shen, H. T. Shen, L. Shao, Binary Multi-View Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(7):1774-1782, 2019. (The first binary code learning method for multi-modal learning.) (CCF A, No. 1 Journal in AI) [Paper][Link][Code]
  2. Z. Zhang, Z. Lai, Z. Huang, W. Wong, G. Xie, L. Liu, L. Shao, Scalable Supervised Asymmetric Hashing with Semantic and Latent Factor Embedding, IEEE Transactions on Image Processing (TIP), 28(10): 4803-4818, 2019. (CCF A)[Link][Code]
  3. Z. Zhang, Z. Lai, Y. Xu, L. Shao, J. Wu, G. Xie, Discriminative Elastic-Net Regularized Linear Regression, IEEE Transactions on Image Processing (TIP), 26(3): 1466-1481, 2017. (CCF A) [Link] [Code][Supplementary]
  4. Z. Zhang, L. Liu, Y. Luo, Z. Huang, F. Shen, H. T. Shen, G. Lu, Inductive Structure Consistent Hashing via Flexible Semantic Calibration, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 32, no. 10, pp. 4514-4528, 2021. [Link]
  5. G. Xie, Z. Zhang, G. Liu, F. Zhu, L. Liu, L. Shao, X. Li, Generalized Zero-Shot Learning with Multiple Graph Adaptive Generative Networks, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 33, no. 7, pp. 2903-2915, 2022. [Link]
  6. Z. Li, Z. Zhang, J. Qin, Z. Zhang, L. Shao, Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 31(3): 786-800, 2020. [Link][Code]
  7. Z. Zhang, L. Shao, Y. Xu, L. Liu, J. Yang, Marginal Representation Learning with Graph Structure Self-Adaptation, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(10): 4645-4659, 2018. [Link][Code]
  8. Z. Zhang, Y. Xu, L. Shao, J. Yang, Discriminative Block-Diagonal Representation Learning for Image Recognition, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(7): 3111-3125, 2018. [Link][Code]
  9. B. Chen, Z. Zhang, Y. Lu, F. Chen, G. Lu, D. Zhang, Semantic-Interactive Graph Convolutional Network for Multilabel Image Recognition, IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMCA), vol. 52, no 8, pp. 4887-4899, 2022. [Link]
  10. Z. Zhang, X. Zhu, G. Lu, Y. Zhang, Probability Ordinal-preserving Semantic Hashing for Large-Scale Image Retrieval, ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 15, no. 3, pp. 37, 2021. [Link][Code]
  11. M. Hou, Z. Zhang, Q. Cao, D. Zhang, G. Lu, Multi-view Speech Emotion Recognition via Collective Relation Construction, IEEE Transactions on Audio, Speech, and Language Processing (TASLP), 30: 218–229, 2022. [Link]
  12. Z. Zhang, X. Wang, G. Lu, F. Shen, L. Zhu, Targeted Attack of Deep Hashing via Prototype-supervised Adversarial Networks, IEEE Transactions on Multimedia (TMM), vol. 24, pp. 3392-3404, 2022. [Link][Code]
  13. Z. Wang, Z. Zhang, Y. Luo, Z. Huang, H. T. Shen, Deep Collaborative Discrete Hashing with Semantic-Invariant Structure Construction, IEEE Transactions on Multimedia (TMM), 23: 1274-1286, 2021. [Link][Code]
  14. B. Chen, Y. Liu, Z. Zhang, Y. Li, Z. Zhang, G. Lu, H. Yu, Deep Active Context Estimation for Automated COVID-19 Diagnosis, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(3s): 1–22, 2021. [Link]
  15. A. Lin, B. Chen, J. Xu, Z. Zhang, G. Lu, DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation, IEEE Transactions on Instrumentation & Measurement (TIM), vol. 71, pp. 1-15, 2022. [Link][Code] (Top 3 Most Popular Paper, Jul. 2022-now)
  16. Y. Luo, Z. Huang, Z. Zhang, Z. Wang, M. Baktashmotlagh, Y. Yang, Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks, in Proc. of The Thirty-Four AAAI Conference on Artificial Intelligence (AAAI), New York, USA, pp. 5021-5028, 2020. (CCF A) [Acceptance Rate: 20.6%]. [Link][Code]
  17. Z. Zhang, G. Xie, Y. Li, S. Li, Z. Huang, SADIH: Semantic-Aware DIscrete Hashing, in Proc. of The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), Hawaii, USA, pp. 5853-5860, 2019. (CCF A) [Acceptance Rate: 16.2%][Link]
  18. Z. Zhang, L. Liu, J. Qin, F. Zhu, F. Shen, Y. Xu, L. Shao, H. T. Shen, Highly-Economized Multi-View Binary Compression for Scalable Image Clustering, in Proc. of The European Conference on Computer Vision (ECCV), Munich, Germany, pp. 731–748, 2018. (CCF B, TOP Conference in Computer Vision) [Oral – Acceptance Rate: 2.4%][Link]