Jaejun Yoo

5.3k total citations · 1 hit paper
39 papers, 1.9k citations indexed

About

Jaejun Yoo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jaejun Yoo has authored 39 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jaejun Yoo's work include Generative Adversarial Networks and Image Synthesis (7 papers), Advanced Image Processing Techniques (6 papers) and Sparse and Compressive Sensing Techniques (5 papers). Jaejun Yoo is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (7 papers), Advanced Image Processing Techniques (6 papers) and Sparse and Compressive Sensing Techniques (5 papers). Jaejun Yoo collaborates with scholars based in South Korea, United States and United Kingdom. Jaejun Yoo's 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 and has published in prestigious journals such as Scientific Reports, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Jaejun Yoo

34 papers receiving 1.8k citations

Hit Papers

StarGAN v2: Diverse Image Synthesis for Multiple Domains 2020 2026 2022 2024 2020 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. StarGAN v2: Diverse Image Synthesis for Multiple Domains
    2020 938 citations Yunjey Choi, Youngjung Uh et al. Scholarworks@UNIST (Ulsan National Institute of Science and Technology) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jaejun Yoo South Korea 10 1.3k 384 262 230 170 39 1.9k
Dmitry Ulyanov Russia 6 1.6k 1.2× 294 0.8× 356 1.4× 243 1.1× 441 2.6× 6 2.5k
Augustus Odena United States 6 1.4k 1.0× 189 0.5× 576 2.2× 106 0.5× 278 1.6× 10 2.1k
Alain Horé Canada 8 2.0k 1.5× 297 0.8× 269 1.0× 299 1.3× 746 4.4× 16 2.8k
Benoît Macq Belgium 21 1.5k 1.1× 194 0.5× 180 0.7× 106 0.5× 93 0.5× 111 2.2k
Meirav Galun Israel 21 1.3k 1.0× 123 0.3× 162 0.6× 100 0.4× 169 1.0× 41 1.8k
A. Blake United Kingdom 12 1.1k 0.8× 127 0.3× 217 0.8× 212 0.9× 72 0.4× 32 1.6k
Mikaël Rousson United States 16 1.4k 1.0× 424 1.1× 224 0.9× 125 0.5× 183 1.1× 23 1.8k
Fabrice Heitz France 22 1.1k 0.9× 348 0.9× 269 1.0× 106 0.5× 250 1.5× 100 1.7k
Fernand S. Cohen United States 21 1.4k 1.0× 199 0.5× 193 0.7× 152 0.7× 325 1.9× 88 1.9k
Andrew Tao United States 6 2.3k 1.7× 154 0.4× 436 1.7× 77 0.3× 241 1.4× 11 2.8k

Countries citing papers authored by Jaejun Yoo

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Jaejun Yoo

This figure shows the co-authorship network connecting the top 25 collaborators of Jaejun Yoo. A scholar is included among the top collaborators of Jaejun Yoo 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 Jaejun Yoo. Jaejun Yoo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Seo, Jung Hwa, et al.. (2025). Deep learning-based classification of diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch. Scientific Reports. 15(1). 5924–5924.
2.
Lee, H., Jung‐Hyun Park, Inpyeong Hwang, et al.. (2025). Deep learning enhances reliability of dynamic contrast-enhanced MRI in diffuse gliomas: bypassing post-processing and providing uncertainty maps. European Radiology. 35(10). 6229–6239. 1 indexed citations
3.
4.
Yoo, Jaejun, et al.. (2024). Enhancing Optical Camera Communication Performance for Collaborative Communication Using Positioning Information. IEEE Access. 12. 11795–11809. 2 indexed citations
5.
6.
Lee, Jae Young, et al.. (2023). Fix the Noise: Disentangling Source Feature for Controllable Domain Translation. 14224–14234. 4 indexed citations
7.
8.
Lee, Giyoung, et al.. (2023). Study on WiFi-based Indoor Positioning Prediction using Machine Learning Techniques. 1471–1474. 1 indexed citations
9.
Pham, Thanh-an, et al.. (2023). Dynamic Fourier ptychography with deep spatiotemporal priors. Inverse Problems. 39(6). 64005–64005. 10 indexed citations
10.
Kim, Minjung, et al.. (2023). Integrated Raw Data Collection and Validation System for Indoor Positioning. 1–6. 1 indexed citations
11.
Sohn, Jy-yong, et al.. (2023). Can We Find Strong Lottery Tickets in Generative Models?. Proceedings of the AAAI Conference on Artificial Intelligence. 37(3). 3267–3275. 2 indexed citations
12.
Kim, Dongyoung, et al.. (2023). Bridging the Domain Gap: A Simple Domain Matching Method for Reference-Based Image Super-Resolution in Remote Sensing. IEEE Geoscience and Remote Sensing Letters. 21. 1–5. 6 indexed citations
13.
Choi, Yunjey, et al.. (2021). Rethinking the Truly Unsupervised Image-to-Image Translation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 14134–14143. 76 indexed citations
14.
Choi, Yunjey, Youngjung Uh, Jaejun Yoo, & Jung-Woo Ha. (2020). StarGAN v2: Diverse Image Synthesis for Multiple Domains. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 8185–8194. 938 indexed citations breakdown →
15.
Lee, Sangwoo, Tong Gao, Sohee Yang, Jaejun Yoo, & Jung-Woo Ha. (2019). Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation. arXiv (Cornell University). 4 indexed citations
16.
Ghahremani, Maryam, et al.. (2018). Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson’s Disease. Journal of Movement Disorders. 11(1). 13–23. 12 indexed citations
17.
Kang, Eun‐Hee, Won Chang, Jaejun Yoo, & Jong Chul Ye. (2018). Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network. IEEE Transactions on Medical Imaging. 37(6). 1358–1369. 235 indexed citations
18.
Yoo, Jaejun, Eun Young Kim, Yong Min Ahn, & Jong Chul Ye. (2016). Topological persistence vineyard for dynamic functional brain connectivity during resting and gaming stages. Journal of Neuroscience Methods. 267. 1–13. 19 indexed citations
19.
Yoo, Jaejun, et al.. (2008). Design and implementation of AWSC (All Ways Stop Control) using magnetic sensor network. 205–209. 1 indexed citations
20.
Liu, Qiang, Jaejun Yoo, Byungtae Jang, Kyoung-Ho Choi, & Jenq–Neng Hwang. (2005). A scalable VideoGIS system for GPS-guided vehicles. Signal Processing Image Communication. 20(3). 205–218. 16 indexed citations

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.

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