Junbum Cha
Impact in
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- Computer Graphics and Visualization Techniques
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- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Video Analysis and Summarization
- Handwritten Text Recognition Techniques
Papers in
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- Generative Adversarial Networks and Image Synthesis 3
- Video Analysis and Summarization 3
- Multimodal Machine Learning Applications 3
- Advanced Image and Video Retrieval Techniques 2
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- Topic Modeling 3
- Domain Adaptation and Few-Shot Learning 2
- Advanced Text Analysis Techniques 2
- Co-authors
- Sanghyuk Chun (5 shared papers)Byungseok Roh (2 shared papers)Jonghwan Mun (2 shared papers)Hyunjung Shim (3 shared papers)Bado Lee (2 shared papers)Kyungjae Lee (1 shared paper)Seonghyun Kim (1 shared paper)Hwalsuk Lee (2 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Lecture notes in computer science (2 papers)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- South KoreaGermanyJapan
In The Last Decade
Junbum Cha
9 papers receiving 377 citations
Peers
Comparison fields: 5 of 40
- Computer Graphics and Computer-Aided Design 70
- Computer Vision and Pattern Recognition 275
- Artificial Intelligence 143
- Health Informatics 2
- Media Technology 12
Countries citing papers authored by Junbum Cha
This map shows the geographic impact of Junbum Cha'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 Junbum Cha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junbum Cha more than expected).
Fields of papers citing papers by Junbum Cha
This network shows the impact of papers produced by Junbum Cha. 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 Junbum Cha. The network helps show where Junbum Cha may publish in the future.
Co-authors
The 15 scholars most cited alongside Junbum Cha, 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 | 2022 | 74 | |
| 2 | 2021 | 67 | |
| 3 | 2023 | 62 | |
| 4 | 2020 | 60 | |
| 5 | 2021 | 59 | |
| 6 | 2024 | 46 | |
| 7 | 2022 | 13 | |
| 8 | 2016 | 3 | |
| 9 | 2016 | 1 | |
| 10 | 2020 | 0 |
About Junbum Cha
Junbum Cha is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Signal Processing and Cancer Research, having authored 10 papers that have together received 385 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Video Analysis and Summarization (3 papers), Topic Modeling (3 papers), Multimodal Machine Learning Applications (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Biomedical Text Mining and Ontologies (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Advanced Text Analysis Techniques (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (70 citations), Computer Vision and Pattern Recognition (275 citations), Artificial Intelligence (143 citations), Health Informatics (2 citations) and Media Technology (12 citations). Junbum Cha has collaborated with scholars based in South Korea, Germany and Japan. Frequent co-authors include Sanghyuk Chun, Byungseok Roh, Jonghwan Mun, Hyunjung Shim, Bado Lee, Kyungjae Lee, Seonghyun Kim, Hwalsuk Lee, Ga‐Young Lee and Sanghyun Park. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Lecture notes in computer science, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.