Zheng Zhang
Associate Professor, Ph.D. Supervisor
Big Media Intelligence (BMI) Research Group
Harbin Institute of Technology, Shenzhen, China
[Google Scholar] [Homepage in Chinese]

Area Chair: ICML/NeurIPS/CVPR/ACM MM
Associate Editor: IEEE T-AC/IEEE J-BHI/INS
Drawing

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 the Harbin Institute of Technology as well as Peng Cheng Laboratory. You also may refer to my BMI research group for detailed information.


Biography

Zheng Zhang received his Ph.D. degree from the Harbin Institute of Technology (HIT), supervised by Prof. Yong Xu (Changjiang Professorship). He visited the Institute of Automation of Chinese Academy of Sciences, Beijing, advised by Prof. Cheng-Lin Liu (IEEE Fellow). After obtaining his doctoral degree, he became an Assistant Researcher at The Hong Kong Polytechnic University (PolyU), and later was a Postdoctoral Research Fellow at Data Science Group, The University of Queensland (UQ), Australia, supervised by Prof. Helen Huang. He was fortunately mentored by Prof. Heng Tao Shen (Member of Academia Europaea, ACM/IEEE Fellow) and Prof. Ling Shao (IEEE Fellow). Since 2019, he has been with School of Computer Science & Technology, Harbin Institute of Technology, Shenzhen, China.

Dr. Zhang's research interests mainly focus on multimedia content understanding, especially multimedia retrieval, multi-modal learning, and big data analysis. He has published over 100 technical papers in prestigious international journals and conference proceedings. Notably, he has been honored with paper awards from ACM Multimedia Asia'21, EAI ICMTEL'22, and SMARTCOMP'14. He is a recipient of Distinguished Ph.D. Dissertation Award of CIE, Outstanding Young Research Achievement Award of CAAI, and Excellent Young Scientists Fund of Shenzhen. His innovative multimedia systems have garnered attention from mainstream media outlets such as UN COP26, Xinhua News, etc., and have been successfully transferred to prominent companies, such as Tencent, Huawei, ICBC, SZIDC, etc. He has been featured as the 'World's Top 2% Scientists' for several consecutive years. He is an IEEE and CCF Senior Member.

Dr. Zheng Zhang serves/served as an Editorial Board Member for IEEE Trans. on Affective Computing (T-AC), IEEE Journal of Biomedical and Health Informatics (J-BHI), Information Sciences (INS), Information Fusion (INFFUS), Information Processing & Management (IP&M), Expert Systems with Applications (ESWA), among others. Additionally, he has contributed as an Area Chair or Senior PC for numerous top conferences, such as ICML, NeurIPS, CVPR, ACM MM, AAAI, IJCAI, etc. He has also demonstrated proficiency in organizing international conferences, such as ADMA 2021 and 2023, ACM Multimedia Asia 2021, along with several other notable events.

Research Interests


Publications (Selected Pub)

Books:
  1. Zheng Zhang, Binary Representation Learning on Visual Images, ISBN: 978-981-97-2111-5, Springer Nature, Jun. 2024. [Link]
  2. 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) [Link]
  3. 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.
  4. 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.
  5. 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.

Journal paper:
  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, X. Yuan, L. Zhu, J. Song, L. Nie, BadCM: Invisible Backdoor Attack against Cross-Modal Learning, IEEE Transactions on Image Processing (TIP), 33: 2558-2571, 2024. (CCF A)[Link][Code]
  3. 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]
  4. 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]
  5. X. Yuan, Z. Zhang, X. Wang, L. Wu, Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval, IEEE Transactions on Information Forensics and Security (TIFS), 18: 4681-4694, 2023. (CCF A) [Link][Code]
  6. Z. Zhang, H. Luo, L. Zhu, G. Lu, H. T. Shen, Modality-Invariant Asymmetric Networks for Cross-Modal Hashing, IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 35, no. 5, pp. 5091-5104, 2023. (CCF A) [Link][Code]
  7. H. Luo, Z. Zhang, L. Nie, Contrastive Incomplete Cross-modal Hashing, IEEE Transactions on Knowledge and Data Engineering (TKDE), DOI: 10.1109/TKDE.2024.3410388, 2024. (CCF A) [Code]
  8. 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] [Code]
  9. 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]
  10. 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]
  11. J. Wen, Z. Zhang, Z. Zhang, L. Fei, M. Wang, Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion, IEEE Transactions on Cybernetics (TCYB), 51(1): 101-114, 2021. [Link][Code]
  12. J. Wen, Z. Zhang, L. Fei, B. Zhang, Y. Xu, Z. Zhang, J. Li, A Survey on Incomplete Multiview Clustering, IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMCA), vol. 53, no. 2, pp. 1136-1149, 2023. [Link][Suppl Doc][Code]
  13. 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]
  14. B. Chen, Y. Liu, Z. Zhang, G. Lu, W.K. Kong, TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), vol. 8, no. 1, pp. 55-68, 2024. [Paper][Link][Code] (Top 3 Most Popular Paper, Jan. 2024-now)
  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)

Conference paper:
  1. Y. Liu, J. Wen, C. Liu, X. Fang, Z. Li, Y. Xu, Z. Zhang, Language-Driven Cross-Modal Classifier for Zero-Shot Multi-Label Image Recognition, in Proc. of The Forty-first International Conference on Machine Learning (ICML), 2024. (CCF A) [Link]
  2. J. Xu, Y. Ren, X. Wang, L. Feng, Z. Zhang, G. Niu, X. Zhu, Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios, in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (CCF A) [Link][Code]
  3. Y. Mo, F. Nie, P. Hu, H. T. Shen, Z. Zhang, X. Wang, X. Zhu, Self-supervised Heterogeneous Graph Learning: a Homogeneity and Heterogeneity Perspective, in Proc. of The Twelfth International Conference on Learning Representations (ICLR), 2024. [Link][Code]
  4. B. Chen, S. Fu, Y. Liu, J. Pan, G. Lu, Z. Zhang, CariesXrays: Enhancing Caries Detection in Hospital-scale Panoramic Dental X-rays via Feature Pyramid Contrastive Learning, in Proc. of The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2024. (CCF A) [Link][Code]
  5. Y. Liu, Z. Wu, B. Chen, G. Lu, Z. Zhang, Medical Cross-Modal Prompt Hashing with Robust Noisy Correspondence Learning, in Proc. of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024.
  6. Z. Zhang, S. Chen, M. Hou, G. Lu, Multimodal Emotion Interaction and Visualization Platform, in Proc. of The 31st ACM International Conference on Multimedia (ACMM), 2023. (CCF A) [Link]
  7. Y. Liu, Q. Wu, Z. Zhang, J. Zhang, G. Lu, Multi-Granularity Interactive Transformer Hashing for Cross-modal Retrieval, in Proc. of The 31st ACM International Conference on Multimedia (ACMM), 2023. (CCF A, Oral) [Link][Code]
  8. X. Wang, Z. Zhang, G. Lu, Y. Xu, Targeted Attack and Defense for Deep Hashing, in Proc. of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 2298-2302, 2021. (CCF A) [Link][Code]
  9. X. Wang, Z. Zhang, B. Wu, F. Shen, G. Lu, Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing, in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16357-16366, 2021. (CCF A) [Link][Code]
  10. J. Wen, Z. Zhang, Z. Zhang, L. Zhu, L. Fei, B. Zhang, Y. Xu, Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring, in Proc. of The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 35(11), pp. 10273-10281, 2021. (CCF A) [Code]
  11. J. Wen, Z. Zhang, Z. Zhang, Z. Wu, L. Fei, Y. Xu, B. Zhang, DIMC-net: Deep Incomplete Multi-view Clustering Network, in Proc. of The 28th ACM International Conference on Multimedia (ACMM), pp. 3753–3761, 2020. (CCF A)
  12. 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]
  13. J. Wen, Z. Zhang, Y. Xu, B. Zhang, L. Fei, H. Liu, Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering, in Proc. of The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), Hawaii, USA, pp. 5393-5400, 2019. (CCF A) [Acceptance Rate: 16.2%][Link][Code]
  14. 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]
  15. 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]

Professional Activities

Journal Editorial Board Membership

Conference Technical Program Committee

Journal Reviewer (20+ IEEE/ACM Trans.):

Research Group

Current Research Students Former Graduate Students (2019- )
Former Undergraduate Students (2020-2021)