Jing Yu Koh
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Computer Graphics and Computer-Aided Design top 5%
- Transportation
- Automotive Engineering
- Co-authors
- Jason BaldridgeHonglak LeeYinfei YangHan ZhangS. K. GhoshPatrick JailletAustin R. WatersYue Zhang
- Topics
- Multimodal Machine Learning Applications (4 papers)Generative Adversarial Networks and Image Synthesis (3 papers)Advanced Vision and Imaging (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignTransportation
- Journals
- Proceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesSingapore
In The Last Decade
Jing Yu Koh
8 papers receiving 315 citations
Hit Papers
Peers
Comparison fields: 5 of 49
- Computer Vision and Pattern Recognition 257
- Artificial Intelligence 83
- Computer Graphics and Computer-Aided Design 39
- Transportation 21
- Automotive Engineering 17
Countries citing papers authored by Jing Yu Koh
This map shows the geographic impact of Jing Yu Koh'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 Jing Yu Koh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing Yu Koh more than expected).
Fields of papers citing papers by Jing Yu Koh
This network shows the impact of papers produced by Jing Yu Koh. 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 Jing Yu Koh. The network helps show where Jing Yu Koh may publish in the future.
Co-authorship network of co-authors of Jing Yu Koh
This figure shows the co-authorship network connecting the top 25 collaborators of Jing Yu Koh. A scholar is included among the top collaborators of Jing Yu Koh 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 Jing Yu Koh. Jing Yu Koh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 20 | |
| 3 | 11 | |
| 4 | 0 | |
| 5 | 12 | |
| 6 | Cross-Modal Contrastive Learning for Text-to-Image Generationbreakdown → | 210 |
| 7 | 35 | |
| 8 | 17 | |
| 9 | 7 |
About Jing Yu Koh
Jing Yu Koh is a scholar working on Transportation, Computer Vision and Pattern Recognition and Geology, having authored 9 papers that have together received 320 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (257 citations), Computer Graphics and Computer-Aided Design (39 citations) and Transportation (21 citations). Jing Yu Koh has collaborated with scholars based in United States and Singapore. Frequent co-authors include Jason Baldridge, Honglak Lee, Yinfei Yang, Han Zhang, S. K. Ghosh, Patrick Jaillet, Austin R. Waters, Yinfei Yang, Yue Zhang and Deqing Sun. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence.
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.