World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
8967
World Ranking
5666
National Ranking
749

Overview

Lei Zhu is affiliated with Tongji University in China and has contributed extensively to the field of Computer Science, with a focus on subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Radiology, Nuclear Medicine and Imaging, as well as Computer Networks and Communications.

Their research encompasses several main topics, such as:

  • Multimodal Machine Learning Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Video Surveillance and Tracking Methods
  • Recommender Systems and Techniques
  • Image Retrieval and Classification Techniques
  • Advanced Graph Neural Networks

Lei Zhu has authored multiple papers including:

  • Deep Collaborative Multi-View Hashing for Large-Scale Image Search, 2020, IEEE Transactions on Image Processing
  • Maximum Density Divergence for Domain Adaptation, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Divergence-Agnostic Unsupervised Domain Adaptation by Adversarial Attacks, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • A Comprehensive Survey on Source-Free Domain Adaptation, 2024, IEEE Transactions on Pattern Analysis and Machine Intelligence

Frequent co-authors of Lei Zhu include:

  • Jingjing Li
  • Zheng Zhang
  • Heng Tao Shen
  • Huaxiang Zhang
  • Zhiyong Cheng

Publications have appeared regularly in venues such as:

  • IEEE Transactions on Circuits and Systems for Video Technology
  • arXiv (Cornell University)
  • ACM Transactions on Information Systems
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Image Processing

In addition to journal articles, Lei Zhu has published a book titled Unsupervised Domain Adaptation in 2024 via Springer Nature.

Best Publications

  • R³Net: Recurrent Residual Refinement Network for Saliency Detection

    Zijun Deng;Xiaowei Hu;Lei Zhu;Lei Zhu;Xuemiao Xu

  • Leveraging the Invariant Side of Generative Zero-Shot Learning

    Jingjing Li;Mengmeng Jing;Ke Lu;Zhengming Ding

  • Maximum Density Divergence for Domain Adaptation

    Jingjing Li;Erpeng Chen;Zhengming Ding;Lei Zhu

  • Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

    Zhiyong Cheng;Ying Ding;Lei Zhu;Mohan Kankanhalli

  • Transfer Independently Together: A Generalized Framework for Domain Adaptation

    Jingjing Li;Ke Lu;Zi Huang;Lei Zhu

  • Interest-aware Message-Passing GCN for Recommendation

    Fan Liu;Zhiyong Cheng;Lei Zhu;Zan Gao

  • Locality Preserving Joint Transfer for Domain Adaptation

    Jingjing Li;Mengmeng Jing;Ke Lu;Lei Zhu

  • MMALFM: Explainable Recommendation by Leveraging Reviews and Images

    Zhiyong Cheng;Xiaojun Chang;Lei Zhu;Rose C. Kanjirathinkal

  • Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval

    Lei Zhu;Jialie Shen;Liang Xie;Zhiyong Cheng

  • Heterogeneous Domain Adaptation Through Progressive Alignment

    Jingjing Li;Ke Lu;Zi Huang;Lei Zhu

  • A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

    Zhiyong Cheng;Ying Ding;Xiangnan He;Lei Zhu

  • Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation

    Zhekai Du;Jingjing Li;Hongzu Su;Lei Zhu

  • Scalable Deep Hashing for Large-Scale Social Image Retrieval

    Hui Cui;Lei Zhu;Jingjing Li;Yang Yang

  • ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning.

    Caixia Yan;Xiaojun Chang;Zhihui Li;Weili Guan

  • Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval

    Lei Zhu;Zi Huang;Zhihui Li;Liang Xie

  • From Zero-Shot Learning to Cold-Start Recommendation

    Jingjing Li;Mengmeng Jing;Ke Lu;Lei Zhu

  • Leveraging the Invariant Side of Generative Zero-Shot Learning

    Jingjing Li;Mengmeng Jin;Ke Lu;Zhengming Ding

  • A Comprehensive Survey on Source-Free Domain Adaptation

    Unknown

  • Deep Collaborative Multi-View Hashing for Large-Scale Image Search

    Lei Zhu;Xu Lu;Zhiyong Cheng;Jingjing Li

  • Discrete Multimodal Hashing With Canonical Views for Robust Mobile Landmark Search

    Lei Zhu;Zi Huang;Xiaobai Liu;Xiangnan He

  • Dynamic Multi-view Hashing for Online Image Retrieval

    Liang Xie;Jialie Shen;Jungong Han;Lei Zhu

  • Online Multi-modal Hashing with Dynamic Query-adaption

    Xu Lu;Lei Zhu;Zhiyong Cheng;Liqiang Nie

  • Divergence-agnostic Unsupervised Domain Adaptation by Adversarial Attacks.

    Jingjing Li;Zhekai Du;Lei Zhu;Zhengming Ding

  • DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation

    Lei Guo;Li Tang;Tong Chen;Lei Zhu

  • Work Together: Correlation-Identity Reconstruction Hashing for Unsupervised Cross-Modal Retrieval

    Unknown

  • MMALFM: Explainable Recommendation by Leveraging Reviews and Images

    Zhiyong Cheng;Xiaojun Chang;Lei Zhu;Rose C. Kanjirathinkal

Frequent Co-Authors

Zhiyong Cheng
Zhiyong Cheng Qilu University of Technology
Huaxiang Zhang
Huaxiang Zhang Shandong Normal University
Zi Huang
Zi Huang University of Queensland
Heng Tao Shen
Heng Tao Shen University of Electronic Science and Technology of China
Liqiang Nie
Liqiang Nie Shandong University
Hai Jin
Hai Jin Huazhong University of Science and Technology
Yang Yang
Yang Yang University of Electronic Science and Technology of China
Zhengming Ding
Zhengming Ding Tulane University
Jialie Shen
Jialie Shen City, University of London
Mohan S. Kankanhalli
Mohan S. Kankanhalli National University of Singapore

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

If you’re interested in Computer Science but seeking flexibility, online degrees offer excellent pathways. Prospective students can find a variety of degree levels online, from bachelor’s to advanced master’s programs. For those wanting to enter the workforce quickly or change fields, exploring the shortest online masters degree options can help you earn credentials in a reduced time frame—sometimes in just one year.

Choosing a program that aligns with current job market demands is essential. Many of the most useful graduate degrees focus on fields like data science, artificial intelligence, and cybersecurity, which offer robust career prospects for Computer Science graduates.

If you’re just beginning your educational journey or need a foundation in technology, an associate degree online can be a cost-effective way to get started. This pathway allows you to build basic skills before pursuing further specialization.

Budget remains an important factor. Fortunately, today’s cheapest online degrees make a quality education more accessible to a wider range of students, without sacrificing academic standards.

Best Scientists Citing Lei Zhu

Trending Scientists