Guanglei Yang
Impact in
- Endocrinology top 5%
- Plant and Fungal Interactions Research
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- Advanced Vision and Imaging
Papers in
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- Multimodal Machine Learning Applications 7
- Advanced Neural Network Applications 4
- Advanced Vision and Imaging 3
- Optical measurement and interference techniques 2
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- Domain Adaptation and Few-Shot Learning 7
- Co-authors
- Jun Meng (6 shared papers)Yushi Luan (6 shared papers)Jun Cui (6 shared papers)Xinxin Hou (5 shared papers)Mingli Ding (8 shared papers)Xiaoxu Zhou (3 shared papers)Elisa Ricci (6 shared papers)Ning Jiang (4 shared papers)
In The Last Decade
Guanglei Yang
25 papers receiving 861 citations
Peers
Comparison fields: 5 of 101
- Endocrinology 152
- Computer Vision and Pattern Recognition 245
- Plant Science 408
- Media Technology 84
- Cancer Research 96
Countries citing papers authored by Guanglei Yang
This map shows the geographic impact of Guanglei Yang'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 Guanglei Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guanglei Yang more than expected).
Fields of papers citing papers by Guanglei Yang
This network shows the impact of papers produced by Guanglei Yang. 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 Guanglei Yang. The network helps show where Guanglei Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Guanglei Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 141 | |
| 2 | 2021 | 132 | |
| 3 | 2018 | 130 | |
| 4 | 2018 | 107 | |
| 5 | 2020 | 85 | |
| 6 | 2020 | 49 | |
| 7 | 2022 | 46 | |
| 8 | 2016 | 25 | |
| 9 | 2022 | 23 | |
| 10 | 2018 | 18 | |
| 11 | Transformers Solve the Limited Receptive Field for Monocular Depth Prediction | 2021 | 16 |
| 12 | 2021 | 14 | |
| 13 | 2023 | 14 | |
| 14 | 2022 | 10 | |
| 15 | 2018 | 10 | |
| 16 | 2015 | 8 | |
| 17 | 2022 | 7 | |
| 18 | 2023 | 6 | |
| 19 | 2024 | 6 | |
| 20 | 2023 | 5 |
About Guanglei Yang
Guanglei Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Plant Science, Environmental Engineering and Building and Construction, having authored 27 papers that have together received 866 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (7 papers), Domain Adaptation and Few-Shot Learning (7 papers), Advanced Neural Network Applications (4 papers), Plant Disease Resistance and Genetics (3 papers), Plant-Microbe Interactions and Immunity (3 papers), Advanced Vision and Imaging (3 papers), Plant and Fungal Interactions Research (2 papers) and Optical measurement and interference techniques (2 papers). The work is most often cited by research in Endocrinology (152 citations), Computer Vision and Pattern Recognition (245 citations), Plant Science (408 citations), Media Technology (84 citations) and Cancer Research (96 citations). Guanglei Yang has collaborated with scholars based in China, Italy and Hong Kong. Frequent co-authors include Jun Meng, Yushi Luan, Jun Cui, Xinxin Hou, Mingli Ding, Xiaoxu Zhou, Elisa Ricci, Ning Jiang, Hao Tang and Nicu Sebe. Their work appears in journals such as Applied Intelligence, The Plant Journal, Sustainability, Energy Economics and Physiological and Molecular Plant Pathology.
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.