Yuren Cong
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Computer Networks and Communications
- Information Systems
- Aerospace Engineering
- Co-authors
- Bodo RosenhahnMichael Ying YangHanno AckermannWentong LiaoJiawei RenTao XiangSen HePing Luo
- Topics
- Multimodal Machine Learning Applications (4 papers)Generative Adversarial Networks and Image Synthesis (3 papers)Advanced Image and Video Retrieval Techniques (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceGeography, Planning and Development
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceInternational Journal of Computer Vision2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Partner nations
- GermanyNetherlandsAustralia
In The Last Decade
Yuren Cong
6 papers receiving 206 citations
Hit Papers
Peers
Comparison fields: 5 of 40
- Computer Vision and Pattern Recognition 162
- Artificial Intelligence 95
- Computer Networks and Communications 11
- Information Systems 10
- Aerospace Engineering 9
Countries citing papers authored by Yuren Cong
This map shows the geographic impact of Yuren Cong'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 Yuren Cong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuren Cong more than expected).
Fields of papers citing papers by Yuren Cong
This network shows the impact of papers produced by Yuren Cong. 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 Yuren Cong. The network helps show where Yuren Cong may publish in the future.
Co-authorship network of co-authors of Yuren Cong
This figure shows the co-authorship network connecting the top 25 collaborators of Yuren Cong. A scholar is included among the top collaborators of Yuren Cong 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 Yuren Cong. Yuren Cong 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 | 2 | |
| 3 | 6 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | RelTR: Relation Transformer for Scene Graph Generationbreakdown → | 104 |
| 9 | 95 |
About Yuren Cong
Yuren Cong is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Biophysics, having authored 9 papers that have together received 211 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (162 citations), Artificial Intelligence (95 citations) and Geography, Planning and Development (8 citations). Yuren Cong has collaborated with scholars based in Germany, Netherlands and Australia. Frequent co-authors include Bodo Rosenhahn, Michael Ying Yang, Hanno Ackermann, Wentong Liao, Jiawei Ren, Tao Xiang, Sen He, Ping Luo, Animesh A. Sinha and Jinhui Yi. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision 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.