Seonguk Seo
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Computational Mechanics
- Computer Graphics and Computer-Aided Design top 5%
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Bohyung HanTackgeun YouSuha KwakSunho ParkBonyong KooJoon‐Young LeeDong-Wan KimSer-Nam Lim
- Topics
- Domain Adaptation and Few-Shot Learning (5 papers)Ship Hydrodynamics and Maneuverability (3 papers)Multimodal Machine Learning Applications (3 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionArtificial Intelligence
- Journals
- Ocean Engineering2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)
- Partner nations
- South KoreaUnited States
In The Last Decade
Seonguk Seo
11 papers receiving 395 citations
Peers
Comparison fields: 5 of 66
- Computer Vision and Pattern Recognition 237
- Artificial Intelligence 187
- Computational Mechanics 49
- Computer Graphics and Computer-Aided Design 49
- Radiology, Nuclear Medicine and Imaging 46
Countries citing papers authored by Seonguk Seo
This map shows the geographic impact of Seonguk Seo'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 Seonguk Seo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seonguk Seo more than expected).
Fields of papers citing papers by Seonguk Seo
This network shows the impact of papers produced by Seonguk Seo. 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 Seonguk Seo. The network helps show where Seonguk Seo may publish in the future.
Co-authorship network of co-authors of Seonguk Seo
This figure shows the co-authorship network connecting the top 25 collaborators of Seonguk Seo. A scholar is included among the top collaborators of Seonguk Seo 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 Seonguk Seo. Seonguk Seo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 12 | |
| 4 | 86 | |
| 5 | 1 | |
| 6 | 13 | |
| 7 | 7 | |
| 8 | Learning to Optimize Domain Specific Normalization with Domain Augmentation for Domain Generalization | 1 |
| 9 | 249 | |
| 10 | Confidence Calibration in Deep Neural Networks through Stochastic Inferences | 1 |
| 11 | 26 | |
| 12 | 5 | |
| 13 | 6 |
About Seonguk Seo
Seonguk Seo is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Ocean Engineering, having authored 13 papers that have together received 407 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Ship Hydrodynamics and Maneuverability (3 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (49 citations), Computer Vision and Pattern Recognition (237 citations) and Artificial Intelligence (187 citations). Seonguk Seo has collaborated with scholars based in South Korea and United States. Frequent co-authors include Bohyung Han, Tackgeun You, Suha Kwak, Sunho Park, Bonyong Koo, Joon‐Young Lee, Dong-Wan Kim, Ser-Nam Lim, Ji-Hye Ha and Sunah Kim. Their work appears in journals such as Ocean Engineering, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).
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.