Chongjian Ge
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Computational Mechanics
- Computer Graphics and Computer-Aided Design top 5%
- Automotive Engineering
- Topics
- Advanced Neural Network Applications (4 papers)Domain Adaptation and Few-Shot Learning (4 papers)Generative Adversarial Networks and Image Synthesis (2 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionComputational Mechanics
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image Processing2021 IEEE/CVF International Conference on Computer Vision (ICCV)
In The Last Decade
Chongjian Ge
9 papers receiving 336 citations
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 264
- Artificial Intelligence 74
- Computational Mechanics 57
- Computer Graphics and Computer-Aided Design 46
- Automotive Engineering 30
Countries citing papers authored by Chongjian Ge
This map shows the geographic impact of Chongjian Ge'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 Chongjian Ge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chongjian Ge more than expected).
Fields of papers citing papers by Chongjian Ge
This network shows the impact of papers produced by Chongjian Ge. 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 Chongjian Ge. The network helps show where Chongjian Ge may publish in the future.
Co-authorship network of co-authors of Chongjian Ge
This figure shows the co-authorship network connecting the top 25 collaborators of Chongjian Ge. A scholar is included among the top collaborators of Chongjian Ge 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 Chongjian Ge. Chongjian Ge is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 35 | |
| 2 | 61 | |
| 3 | 26 | |
| 4 | 18 | |
| 5 | 1 | |
| 6 | Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning | 8 |
| 7 | 35 | |
| 8 | 102 | |
| 9 | 64 |
About Chongjian Ge
Chongjian Ge is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering, having authored 9 papers that have together received 350 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (46 citations), Computer Vision and Pattern Recognition (264 citations) and Computational Mechanics (57 citations). Chongjian Ge has collaborated with scholars based in Hong Kong, China and Sweden. Frequent co-authors include Ping Luo, Yibing Song, Wei Liu, Yuying Ge, Enze Xie, Ruimao Zhang, Han Yang, Shoufa Chen, Ding Liang and Runjian Chen. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing 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.