Junran Peng
Impact in
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- Advanced Neural Network Applications
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Speech and dialogue systems
Papers in
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- Advanced Neural Network Applications 12
- Medical Image Segmentation Techniques 1
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- 3D Shape Modeling and Analysis 6
- Co-authors
- Zhaoxiang Zhang (17 shared papers)Yuxi Wang (6 shared papers)Tieniu Tan (3 shared papers)Chuanchen Luo (9 shared papers)Xingyuan Bu (4 shared papers)Lue Fan (2 shared papers)Yang Liu (1 shared paper)Naiyan Wang (1 shared paper)
In The Last Decade
Junran Peng
29 papers receiving 510 citations
Junran Peng's Hit Papers
Peers
Comparison fields: 5 of 57
- Computer Vision and Pattern Recognition 188
- Artificial Intelligence 145
- Computer Graphics and Computer-Aided Design 19
- Computational Mechanics 72
- Automotive Engineering 25
Countries citing papers authored by Junran Peng
This map shows the geographic impact of Junran Peng'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 Junran Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junran Peng more than expected).
Fields of papers citing papers by Junran Peng
This network shows the impact of papers produced by Junran Peng. 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 Junran Peng. The network helps show where Junran Peng may publish in the future.
Co-authors
The 25 scholars most cited alongside Junran Peng, 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 32 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 88 | |
| 2 | CityGaussian: Real-Time High-Quality Large-Scale Scene Rendering with Gaussians Hit paper breakdown → | 2024 | 80 |
| 3 | 2020 | 52 | |
| 4 | 2024 | 48 | |
| 5 | 2021 | 32 | |
| 6 | 2024 | 29 | |
| 7 | 2021 | 26 | |
| 8 | 2019 | 22 | |
| 9 | 2023 | 18 | |
| 10 | 2015 | 15 | |
| 11 | 2024 | 13 | |
| 12 | 2016 | 12 | |
| 13 | 2024 | 12 | |
| 14 | 2023 | 11 | |
| 15 | 2022 | 7 | |
| 16 | 2022 | 7 | |
| 17 | 2025 | 7 | |
| 18 | 2016 | 6 | |
| 19 | 2017 | 6 | |
| 20 | 2024 | 6 |
About Junran Peng
Junran Peng is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Control and Systems Engineering, Artificial Intelligence and Biomedical Engineering, having authored 32 papers that have together received 521 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (12 papers), 3D Shape Modeling and Analysis (6 papers), Domain Adaptation and Few-Shot Learning (2 papers), Power Systems Fault Detection (2 papers), Medical Imaging and Analysis (2 papers), Human Motion and Animation (2 papers), Medical Image Segmentation Techniques (1 paper) and Analog and Mixed-Signal Circuit Design (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (188 citations), Artificial Intelligence (145 citations), Computer Graphics and Computer-Aided Design (19 citations), Computational Mechanics (72 citations) and Automotive Engineering (25 citations). Junran Peng has collaborated with scholars based in China, India and Hong Kong. Frequent co-authors include Zhaoxiang Zhang, Yuxi Wang, Tieniu Tan, Chuanchen Luo, Xingyuan Bu, Lue Fan, Yang Liu, Naiyan Wang, Ming Sun and Cong Pan. Their work appears in journals such as International Journal of Computer Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, IET Generation Transmission & Distribution, Journal of Dentistry and Pattern Recognition.
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.