Lin Gu
- Health Informatics top 2%
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- Image Enhancement Techniques 9
- Multimodal Machine Learning Applications 8
- Advanced Image and Video Retrieval Techniques 7
- Image and Signal Denoising Methods 6
- Media Technology top 1%
- Advanced Image Fusion Techniques 8
- Remote-Sensing Image Classification 7
- Artificial Intelligence top 2%
- Domain Adaptation and Few-Shot Learning 8
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- Color Science and Applications 7
Lin Gu
80 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 166
- Health Informatics 66
- Computer Vision and Pattern Recognition 921
- Media Technology 367
- Artificial Intelligence 506
- Radiology, Nuclear Medicine and Imaging 269
Countries citing papers authored by Lin Gu
This map shows the geographic impact of Lin Gu'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 Lin Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lin Gu more than expected).
Fields of papers citing papers by Lin Gu
This network shows the impact of papers produced by Lin Gu. 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 Lin Gu. The network helps show where Lin Gu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lin Gu, 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 | 2024 | 7 | |
| 3 | 2024 | 12 | |
| 4 | 2024 | 2 | |
| 5 | Frequency-Aware Feature Fusion for Dense Image Predictionbreakdown → | 2024 | 59 |
| 6 | 2024 | 9 | |
| 7 | 2024 | 1 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 14 | |
| 10 | 2023 | 4 | |
| 11 | 2022 | 56 | |
| 12 | 2022 | 6 | |
| 13 | 2022 | 1 | |
| 14 | 2020 | 16 | |
| 15 | bumjun_jung at VQA-Med 2020: VQA Model Based on Feature Extraction and Multi-modal Feature Fusion. | 2020 | 2 |
| 16 | Understanding adversarial attacks on deep learning based medical image analysis systemsbreakdown → | 2020 | 304 |
| 17 | Zhejiang University at ImageCLEF 2019 Visual Question Answering in the Medical Domain. | 2019 | 10 |
| 18 | Shadow detection via Rayleigh scattering and Mie theory | 2012 | 2 |
| 19 | Research on the Adaptability of Conventional UFLS/UVLS Criteria for Interconnected Power Systems | 2009 | 2 |
| 20 | Corner Tracking for Dynamic Scene Analysis | 1994 | 1 |
About Lin Gu
Lin Gu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Health Informatics, having authored 83 papers that have together received 2.5k indexed citations. Recurring topics across this work include Image Enhancement Techniques (9 papers), Domain Adaptation and Few-Shot Learning (8 papers), Advanced Image Fusion Techniques (8 papers), Multimodal Machine Learning Applications (8 papers), Color Science and Applications (7 papers), Remote-Sensing Image Classification (7 papers), Advanced Image and Video Retrieval Techniques (7 papers) and Image and Signal Denoising Methods (6 papers). The work is most often cited by research in Health Informatics (66 citations), Computer Vision and Pattern Recognition (921 citations) and Media Technology (367 citations). Lin Gu has collaborated with scholars based in Japan, China and Australia. Frequent co-authors include Barrett J. Rollins, Susan Tseng, Jun Zhou, Xiao Bai, Tatsuya Harada, Yisen Wang, James Bailey, Yitian Zhao, Feng Lu and Xingjun Ma. Their work appears in journals such as Medical Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems and BMC Psychiatry.
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.