Pengzhen Ren
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Control and Systems Engineering top 10%
- Electrical and Electronic Engineering
- Media Technology top 5%
- Topics
- Advanced Neural Network Applications (4 papers)Domain Adaptation and Few-Shot Learning (4 papers)Face and Expression Recognition (3 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceACM Computing SurveysIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- AustraliaChinaUnited States
In The Last Decade
Pengzhen Ren
11 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 711
- Computer Vision and Pattern Recognition 536
- Control and Systems Engineering 90
- Electrical and Electronic Engineering 89
- Media Technology 78
Countries citing papers authored by Pengzhen Ren
This map shows the geographic impact of Pengzhen 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 Pengzhen Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pengzhen Ren more than expected).
Fields of papers citing papers by Pengzhen Ren
This network shows the impact of papers produced by Pengzhen 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 Pengzhen Ren. The network helps show where Pengzhen Ren may publish in the future.
Co-authorship network of co-authors of Pengzhen Ren
This figure shows the co-authorship network connecting the top 25 collaborators of Pengzhen Ren. A scholar is included among the top collaborators of Pengzhen Ren based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pengzhen Ren. Pengzhen Ren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 17 | |
| 4 | 12 | |
| 5 | 29 | |
| 6 | 9 | |
| 7 | A Comprehensive Survey of Neural Architecture Searchbreakdown → | 346 |
| 8 | A Comprehensive Survey of Scene Graphs: Generation and Applicationbreakdown → | 202 |
| 9 | A Survey of Deep Active Learningbreakdown → | 628 |
| 10 | 1 | |
| 11 | 14 | |
| 12 | 39 |
About Pengzhen Ren
Pengzhen Ren is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Urban Studies, having authored 12 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (536 citations), Artificial Intelligence (711 citations) and Media Technology (78 citations). Pengzhen Ren has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Xiaojun Chang, Zhihui Li, Yun Xiao, Po-Yao Huang, Xiaojiang Chen, Xin Wang, Brij B. Gupta, Xiaojiang Chen, Xin Wang and Pengfei Xu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Computing Surveys and IEEE Transactions on Neural Networks and Learning Systems.
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.