Youngmin Kim
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Computational Mechanics top 10%
- Signal Processing top 10%
- Computer Graphics and Computer-Aided Design top 5%
- Co-authors
- Amitabh VarshneySung Woo ChoiSeong‐Whan LeeDavid W. JacobsFrançois GuimbretièreChan-Gun LeeKi-Seong LeeAlice Oh
- Topics
- Technology and Data Analysis (9 papers)Embedded Systems Design Techniques (5 papers)Visual Attention and Saliency Detection (4 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionHealth Informatics
- Partner nations
- South KoreaUnited StatesFrance
In The Last Decade
Youngmin Kim
37 papers receiving 421 citations
Peers
Comparison fields: 5 of 99
- Computer Vision and Pattern Recognition 271
- Artificial Intelligence 71
- Computational Mechanics 67
- Signal Processing 56
- Computer Graphics and Computer-Aided Design 50
Countries citing papers authored by Youngmin Kim
This map shows the geographic impact of Youngmin Kim'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 Youngmin Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Youngmin Kim more than expected).
Fields of papers citing papers by Youngmin Kim
This network shows the impact of papers produced by Youngmin Kim. 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 Youngmin Kim. The network helps show where Youngmin Kim may publish in the future.
Co-authorship network of co-authors of Youngmin Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Youngmin Kim. A scholar is included among the top collaborators of Youngmin Kim 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 Youngmin Kim. Youngmin Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 0 | |
| 6 | 25 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 0 | |
| 10 | 1 | |
| 11 | MMGAN: Manifold Matching Generative Adversarial Network for Generating Images. | 2 |
| 12 | 1 | |
| 13 | Deep Neural Networks for identifying noise transients in Gravitational-Wave Detectors | 1 |
| 14 | 4 | |
| 15 | 3 | |
| 16 | 0 | |
| 17 | 33 | |
| 18 | 83 | |
| 19 | A Study on the Implementation Activation and Development Strategies of SCM | 1 |
| 20 | 89 |
About Youngmin Kim
Youngmin Kim is a scholar working on Hardware and Architecture, Computer Graphics and Computer-Aided Design and Health Informatics, having authored 46 papers that have together received 446 indexed citations. Recurring topics across this work include Technology and Data Analysis (9 papers), Embedded Systems Design Techniques (5 papers) and Visual Attention and Saliency Detection (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (50 citations), Computer Vision and Pattern Recognition (271 citations) and Health Informatics (15 citations). Youngmin Kim has collaborated with scholars based in South Korea, United States and France. Frequent co-authors include Amitabh Varshney, Sung Woo Choi, Seong‐Whan Lee, David W. Jacobs, François Guimbretière, Chan-Gun Lee, Ki-Seong Lee, Alice Oh, Jong-Ho Kim and Hyung Joon Joo. Their work appears in journals such as Sensors, Frontiers in Pharmacology and IEEE Transactions on Visualization and Computer Graphics.
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.