Jaejun Yoo
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- Generative Adversarial Networks and Image Synthesis 7
- Advanced Image Processing Techniques 6
- Multimodal Machine Learning Applications 4
- Image and Signal Denoising Methods 3
- Media Technology top 2%
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- Medical Imaging Techniques and Applications 3
- Radiomics and Machine Learning in Medical Imaging 3
- Artificial Intelligence top 5%
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- Sparse and Compressive Sensing Techniques 5
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- Indoor and Outdoor Localization Technologies 5
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignMedia Technology
- Journals
- Scientific Reports (1 paper)IEEE Geoscience and Remote Sensing Letters (1 paper)Signal Processing Image Communication (1 paper)
- Partner nations
- South KoreaUnited StatesUnited Kingdom
In The Last Decade
Jaejun Yoo
34 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 117
- Computer Vision and Pattern Recognition 1.3k
- Computer Graphics and Computer-Aided Design 156
- Media Technology 170
- Radiology, Nuclear Medicine and Imaging 384
- Artificial Intelligence 262
Countries citing papers authored by Jaejun Yoo
This map shows the geographic impact of Jaejun Yoo'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 Jaejun Yoo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaejun Yoo more than expected).
Fields of papers citing papers by Jaejun Yoo
This network shows the impact of papers produced by Jaejun Yoo. 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 Jaejun Yoo. The network helps show where Jaejun Yoo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jaejun Yoo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 0 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 10 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 6 | |
| 13 | 2021 | 76 | |
| 14 | StarGAN v2: Diverse Image Synthesis for Multiple Domainsbreakdown → | 2020 | 938 |
| 15 | 2019 | 4 | |
| 16 | 2018 | 12 | |
| 17 | 2018 | 235 | |
| 18 | 2016 | 19 | |
| 19 | 2008 | 1 | |
| 20 | 2005 | 16 |
About Jaejun Yoo
Jaejun Yoo is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Media Technology, Radiology, Nuclear Medicine and Imaging and Instrumentation, having authored 39 papers that have together received 1.9k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (7 papers), Advanced Image Processing Techniques (6 papers), Sparse and Compressive Sensing Techniques (5 papers), Indoor and Outdoor Localization Technologies (5 papers), Multimodal Machine Learning Applications (4 papers), Medical Imaging Techniques and Applications (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Image and Signal Denoising Methods (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Computer Graphics and Computer-Aided Design (156 citations), Media Technology (170 citations), Radiology, Nuclear Medicine and Imaging (384 citations) and Artificial Intelligence (262 citations). Jaejun Yoo has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent co-authors include Youngjung Uh, Jung-Woo Ha, Yunjey Choi, Jong Chul Ye, Eun‐Hee Kang, Won Chang, Woong Bae, Dongwook Lee, Sanghyuk Chun and Hyunjung Shim. Their work appears in journals such as Scientific Reports, IEEE Geoscience and Remote Sensing Letters, Signal Processing Image Communication, SIAM Journal on Imaging Sciences and IEEE Access.
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.