Jiefeng Peng
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Guangrun WangXiaodan LiangChanglin LiXiaojun ChangLiang LinBing WangXinjiang WangPing Luo
- Topics
- Domain Adaptation and Few-Shot Learning (5 papers)Advanced Neural Network Applications (4 papers)Advanced Image and Video Retrieval Techniques (2 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceFrontiers of Computer Science2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Partner nations
- ChinaAustraliaUnited Kingdom
In The Last Decade
Jiefeng Peng
7 papers receiving 200 citations
Peers
Comparison fields: 5 of 52
- Computer Vision and Pattern Recognition 150
- Artificial Intelligence 119
- Electrical and Electronic Engineering 17
- Computer Networks and Communications 10
- Signal Processing 10
Countries citing papers authored by Jiefeng Peng
This map shows the geographic impact of Jiefeng 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 Jiefeng Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiefeng Peng more than expected).
Fields of papers citing papers by Jiefeng Peng
This network shows the impact of papers produced by Jiefeng 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 Jiefeng Peng. The network helps show where Jiefeng Peng may publish in the future.
Co-authorship network of co-authors of Jiefeng Peng
This figure shows the co-authorship network connecting the top 25 collaborators of Jiefeng Peng. A scholar is included among the top collaborators of Jiefeng Peng 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 Jiefeng Peng. Jiefeng Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 54 | |
| 3 | 9 | |
| 4 | 111 | |
| 5 | Kalman Normalization: Normalizing Internal Representations Across Network Layers | 17 |
| 6 | 8 | |
| 7 | 2 |
About Jiefeng Peng
Jiefeng Peng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cancer Research, having authored 7 papers that have together received 202 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Advanced Neural Network Applications (4 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (150 citations), Artificial Intelligence (119 citations) and Media Technology (9 citations). Jiefeng Peng has collaborated with scholars based in China, Australia and United Kingdom. Frequent co-authors include Guangrun Wang, Xiaodan Liang, Changlin Li, Xiaojun Chang, Liang Lin, Bing Wang, Xinjiang Wang, Ping Luo, Ruimao Zhang and Guanbin Li. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Frontiers of Computer Science and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.