Junbum Cha
Impact in
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- Computer Graphics and Visualization Techniques
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- Generative Adversarial Networks and Image Synthesis
- Video Analysis and Summarization
- Handwritten Text Recognition Techniques
- Multimodal Machine Learning Applications
- Advanced Vision and Imaging
Papers in
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- Topic Modeling 3
- Advanced Text Analysis Techniques 2
- Natural Language Processing Techniques 2
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- Advanced Image and Video Retrieval Techniques 2
- Video Analysis and Summarization 2
- Multimodal Machine Learning Applications 2
- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Sanghyuk Chun (3 shared papers)Hyunjung Shim (3 shared papers)Byungseok Roh (2 shared papers)Jonghwan Mun (2 shared papers)Bado Lee (1 shared paper)Sanghyun Park (2 shared papers)Sungrae Park (1 shared paper)Ji‐Hoon Kim (1 shared paper)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- South KoreaJapanGermany
In The Last Decade
Junbum Cha
7 papers receiving 151 citations
Peers
Comparison fields: 5 of 28
- Computer Graphics and Computer-Aided Design 47
- Computer Vision and Pattern Recognition 114
- Artificial Intelligence 43
- Health Informatics 1
- Control and Systems Engineering 14
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 12 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 | 2021 | 49 | |
| 2 | 2021 | 41 | |
| 3 | 2023 | 33 | |
| 4 | 2024 | 19 | |
| 5 | 2022 | 11 | |
| 6 | 2016 | 3 | |
| 7 | 2016 | 1 | |
| 8 | 2020 | 0 |
About Junbum Cha
Junbum Cha is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Signal Processing and Media Technology, having authored 8 papers that have together received 157 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Advanced Text Analysis Techniques (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Natural Language Processing Techniques (2 papers), Video Analysis and Summarization (2 papers), Multimodal Machine Learning Applications (2 papers), Biomedical Text Mining and Ontologies (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (47 citations), Computer Vision and Pattern Recognition (114 citations), Artificial Intelligence (43 citations), Health Informatics (1 citation) and Control and Systems Engineering (14 citations). Junbum Cha has collaborated with scholars based in South Korea, Japan and Germany. Frequent co-authors include Sanghyuk Chun, Hyunjung Shim, Byungseok Roh, Jonghwan Mun, Bado Lee, Sanghyun Park, Sungrae Park, Ji‐Hoon Kim, Geewook Kim and Junyeop Lee. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 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.