Minjie Ren
Impact in
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- Emotion and Mood Recognition
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- Human Pose and Action Recognition
Papers in
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- Emotion and Mood Recognition 8
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- Sentiment Analysis and Opinion Mining 6
- Topic Modeling 4
- Co-authors
- Weizhi Nie (7 shared papers)An-An Liu (5 shared papers)Jie Nie (3 shared papers)Xiangdong Huang (5 shared papers)Yuting Su (3 shared papers)Sicheng Zhao (1 shared paper)Zhendong Mao (1 shared paper)Wenhui Li (2 shared papers)
- Journals
- IEEE Transactions on Multimedia (4 papers)IEEE Access (2 papers)Journal of Visual Communication and Image Representation (1 paper)Visual Informatics (1 paper)IEEE Signal Processing Letters (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Minjie Ren
11 papers receiving 275 citations
Peers
Comparison fields: 5 of 46
- Experimental and Cognitive Psychology 157
- Computer Vision and Pattern Recognition 94
- Artificial Intelligence 131
- Signal Processing 42
- Computer Graphics and Computer-Aided Design 7
Countries citing papers authored by Minjie Ren
This map shows the geographic impact of Minjie Ren'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 Minjie Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minjie Ren more than expected).
Fields of papers citing papers by Minjie Ren
This network shows the impact of papers produced by Minjie Ren. 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 Minjie Ren. The network helps show where Minjie Ren may publish in the future.
Co-authors
The 19 scholars most cited alongside Minjie Ren, 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 | 2020 | 60 | |
| 2 | 2020 | 44 | |
| 3 | 2021 | 43 | |
| 4 | 2021 | 40 | |
| 5 | 2019 | 26 | |
| 6 | 2023 | 21 | |
| 7 | 2021 | 21 | |
| 8 | 2021 | 13 | |
| 9 | 2020 | 5 | |
| 10 | 2023 | 2 | |
| 11 | 2020 | 1 |
About Minjie Ren
Minjie Ren is a scholar working on Experimental and Cognitive Psychology, Artificial Intelligence, Computer Vision and Pattern Recognition, Social Psychology and Signal Processing, having authored 11 papers that have together received 276 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (8 papers), Sentiment Analysis and Opinion Mining (6 papers), Topic Modeling (4 papers), Human Pose and Action Recognition (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), 3D Shape Modeling and Analysis (2 papers), 3D Surveying and Cultural Heritage (1 paper) and Color perception and design (1 paper). The work is most often cited by research in Experimental and Cognitive Psychology (157 citations), Computer Vision and Pattern Recognition (94 citations), Artificial Intelligence (131 citations), Signal Processing (42 citations) and Computer Graphics and Computer-Aided Design (7 citations). Minjie Ren has collaborated with scholars based in China and United States. Frequent co-authors include Weizhi Nie, An-An Liu, Jie Nie, Xiangdong Huang, Yuting Su, Sicheng Zhao, Zhendong Mao, Wenhui Li, Dan Song and Jing Liu. Their work appears in journals such as IEEE Transactions on Multimedia, IEEE Access, Journal of Visual Communication and Image Representation, Visual Informatics and IEEE Signal Processing Letters.
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.