Zheng Zhang
Harbin Institute of Technology, University of Queensland, Hong Kong Polytechnic University
About
Dr. Zheng Zhang is currently with Harbin Institute of Technology, Shenzhen, China. His research interests include Machine Learning and Multimedia, with particular emphasis on multimodal learning, efficient deep learning, and AI security. He has published over 200 technical papers in leading international journals and conferences. Dr. Zhang serves or has served on the editorial boards of several prominent journals, including IEEE TIP, IEEE TIFS, IEEE TAFFC, and Elsevier journals such as INFFUS, IP&M, and INS. He has regularly contributed as an Area Chair for top-tier AI conferences, such as ICML, NeurIPS, CVPR, ICLR, AAAI, and ACM MM.
Employment
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Harbin Institute of Technology Professor2024 - Present
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Harbin Institute of Technology Associate Professor2019 - 2023
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University of Queensland Research Fellow2018 - 2019
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Hong Kong Polytechnic University Research Associate2018 - 2018
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Chinese Academy of Sciences Visiting Researcher2015 - 2016
Education
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Harbin Institute of Technology PH.D.2014 - 2018
Projects & Funding
Projects & funding information is unavailable.
Publications (260)
- Grading-inspired complementary enhancing for multimodal sentiment analysis Save
- Dual-Attention Video Representation Learning for Parameter Efficient Text-Video Retrieval Save
- Spatiotemporal-Grounded Vision-Language Reasoning for Compositional Zero-Shot Surgical Video Understanding Save
- Contextual Interaction via Primitive-based Adversarial Training for Compositional Zero-shot Learning Save
- Guest Editorial: Domain Adaptation and Generalization for Biomedical and Health Informatics Save
- DMLoRA: Dynamic Multi-Subspace Low-Rank Adaptation Save
- Selective Multi-grained Alignment for Text-Video Retrieval Save
- Supervised Information Mining From Weakly Paired Images for Breast IHC Virtual Staining Save
- Untargeted Closed-Box Attack Against Healthcare Image Retrieval via Rank Manipulation Save
- Attribute Prompt Alignment Network for Zero-Shot Learning Save