Minsu Cho
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- Human Pose and Action Recognition 13
- Advanced Image and Video Retrieval Techniques 13
- Multimodal Machine Learning Applications 11
- Advanced Neural Network Applications 5
- Artificial Intelligence top 1%
- Domain Adaptation and Few-Shot Learning 9
- Anomaly Detection Techniques and Applications 6
- Media Technology top 2%
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- Business Process Modeling and Analysis 10
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- Data Quality and Management 5
Minsu Cho
73 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Computer Vision and Pattern Recognition 1.8k
- Artificial Intelligence 1.1k
- Media Technology 215
- Management Information Systems 142
- Industrial and Manufacturing Engineering 103
Countries citing papers authored by Minsu Cho
This map shows the geographic impact of Minsu Cho'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 Minsu Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minsu Cho more than expected).
Fields of papers citing papers by Minsu Cho
This network shows the impact of papers produced by Minsu Cho. 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 Minsu Cho. The network helps show where Minsu Cho may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Minsu Cho, 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 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 9 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 0 | |
| 8 | 2023 | 9 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 16 | |
| 11 | 2022 | 6 | |
| 12 | 2021 | 7 | |
| 13 | 2021 | 1 | |
| 14 | 2020 | 179 | |
| 15 | 2019 | 6 | |
| 16 | InstaGAN: Instance-aware Image-to-Image Translation | 2018 | 26 |
| 17 | 2018 | 209 | |
| 18 | 2017 | 167 | |
| 19 | 2016 | 9 | |
| 20 | 2015 | 6 |
About Minsu Cho
Minsu Cho is a scholar working on Computer Vision and Pattern Recognition, Management Information Systems, Artificial Intelligence, Emergency Medical Services and Management Science and Operations Research, having authored 79 papers that have together received 2.9k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (13 papers), Advanced Image and Video Retrieval Techniques (13 papers), Multimodal Machine Learning Applications (11 papers), Business Process Modeling and Analysis (10 papers), Domain Adaptation and Few-Shot Learning (9 papers), Anomaly Detection Techniques and Applications (6 papers), Advanced Neural Network Applications (5 papers) and Data Quality and Management (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.8k citations), Artificial Intelligence (1.1k citations), Media Technology (215 citations), Management Information Systems (142 citations) and Industrial and Manufacturing Engineering (103 citations). Minsu Cho has collaborated with scholars based in South Korea, United States and Germany. Frequent co-authors include Yan Lu, Bohyung Han, Suha Kwak, Sung‐Yeon Kim, Jonghwan Mun, Minseok Song, Dong-Won Kim, Bumsub Ham, Jean Ponce and Sooyoung Yoo. Their work appears in journals such as International Journal of Medical Informatics, IEEE Transactions on Pattern Analysis and Machine Intelligence, PLoS ONE, Scientific Reports and IEEE Transactions on Semiconductor Manufacturing.
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.