Linlin Shen
Impact in
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- Face and Expression Recognition
- Face recognition and analysis
- Media Technology top 0.2%
- Remote-Sensing Image Classification
Papers in
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- Face recognition and analysis 73
- Face and Expression Recognition 63
- Generative Adversarial Networks and Image Synthesis 28
- Advanced Neural Network Applications 23
- Digital Imaging for Blood Diseases 19
Linlin Shen
285 papers receiving 6.7k citations
Hit Papers
Peers
Comparison fields: 5 of 188
- Computer Vision and Pattern Recognition 3.4k
- Media Technology 1.1k
- Computational Mathematics 52
- Signal Processing 640
- Artificial Intelligence 1.7k
Countries citing papers authored by Linlin Shen
This map shows the geographic impact of Linlin Shen'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 Linlin Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linlin Shen more than expected).
Fields of papers citing papers by Linlin Shen
This network shows the impact of papers produced by Linlin Shen. 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 Linlin Shen. The network helps show where Linlin Shen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Linlin Shen, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 3 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 3 | |
| 12 | 2023 | 11 | |
| 13 | 2023 | 58 | |
| 14 | 2022 | 8 | |
| 15 | 2021 | 50 | |
| 16 | 2021 | 49 | |
| 17 | Brain-inspired computing with memristors: Challenges in devices, circuits, and systems Hit paper breakdown → | 2020 | 289 |
| 18 | 2020 | 97 | |
| 19 | ALK-rearrangement neuroendocrine carcinoma of the lung: a comprehensive study of a rare case series and review of literature | 2018 | 3 |
| 20 | 2018 | 23 |
About Linlin Shen
Linlin Shen is a scholar working on Computer Vision and Pattern Recognition, Computational Mathematics, Media Technology, Signal Processing and Artificial Intelligence, having authored 311 papers that have together received 6.8k indexed citations. Recurring topics across this work include Face recognition and analysis (73 papers), Face and Expression Recognition (63 papers), Biometric Identification and Security (39 papers), Generative Adversarial Networks and Image Synthesis (28 papers), AI in cancer detection (24 papers), Advanced Neural Network Applications (23 papers), Radiomics and Machine Learning in Medical Imaging (19 papers) and Digital Imaging for Blood Diseases (19 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.4k citations), Media Technology (1.1k citations), Computational Mathematics (52 citations), Signal Processing (640 citations) and Artificial Intelligence (1.7k citations). Linlin Shen has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Yuexiang Li, Xianxu Hou, Meng Yang, Sen Jia, Li Bai, S. Jia, Siyang Song, Feng Liu, Weicheng Xie and Zhen Ji. Their work appears in journals such as Pattern Recognition, Neurocomputing, IEEE Transactions on Multimedia, Expert Systems with Applications and IEEE Transactions on Information Forensics and Security.
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.