Linjun Zhou
Impact in
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Machine Learning and ELM
- Machine Learning and Data Classification
- Topic Modeling
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
Papers in
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- Domain Adaptation and Few-Shot Learning 7
- Adversarial Robustness in Machine Learning 2
- Anomaly Detection Techniques and Applications 1
- Machine Learning and Algorithms 1
- Gaussian Processes and Bayesian Inference 1
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- Multimodal Machine Learning Applications 3
- Advanced Neural Network Applications 2
- Advanced Image and Video Retrieval Techniques 1
- Co-authors
- Peng Cui (7 shared papers)Zheyan Shen (4 shared papers)Xingxuan Zhang (3 shared papers)Renzhe Xu (2 shared papers)Yue He (1 shared paper)Qi Tian (2 shared papers)Shiqiang Yang (2 shared papers)Xu Jia (1 shared paper)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
In The Last Decade
Linjun Zhou
7 papers receiving 252 citations
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 178
- Computer Vision and Pattern Recognition 106
- Media Technology 14
- Signal Processing 12
- Statistics and Probability 9
Countries citing papers authored by Linjun Zhou
This map shows the geographic impact of Linjun Zhou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Linjun Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linjun Zhou more than expected).
Fields of papers citing papers by Linjun Zhou
This network shows the impact of papers produced by Linjun Zhou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Linjun Zhou. The network helps show where Linjun Zhou may publish in the future.
Co-authors
The 15 scholars most cited alongside Linjun Zhou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 173 | |
| 2 | 2022 | 27 | |
| 3 | 2020 | 18 | |
| 4 | 2021 | 15 | |
| 5 | 2019 | 11 | |
| 6 | 2022 | 9 | |
| 7 | 2022 | 5 |
About Linjun Zhou
Linjun Zhou is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Statistics and Probability and Infectious Diseases, having authored 7 papers that have together received 258 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (7 papers), Multimodal Machine Learning Applications (3 papers), Adversarial Robustness in Machine Learning (2 papers), Advanced Neural Network Applications (2 papers), Anomaly Detection Techniques and Applications (1 paper), Machine Learning and Algorithms (1 paper), Advanced Image and Video Retrieval Techniques (1 paper) and Gaussian Processes and Bayesian Inference (1 paper). The work is most often cited by research in Artificial Intelligence (178 citations), Computer Vision and Pattern Recognition (106 citations), Media Technology (14 citations), Signal Processing (12 citations) and Statistics and Probability (9 citations). Linjun Zhou has collaborated with scholars based in China and Sweden. Frequent co-authors include Peng Cui, Zheyan Shen, Xingxuan Zhang, Renzhe Xu, Yue He, Qi Tian, Shiqiang Yang, Xu Jia, Kun Kuang and Jiashuo Liu. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Proceedings of the AAAI Conference on Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.