Mingi Ji
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Handwritten Text Recognition Techniques
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Natural Language Processing Techniques
Papers in
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- Topic Modeling 3
- Domain Adaptation and Few-Shot Learning 3
- Adversarial Robustness in Machine Learning 2
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- Multimodal Machine Learning Applications 2
- Handwritten Text Recognition Techniques 2
- Advanced Neural Network Applications 2
- Co-authors
- Il‐Chul Moon (6 shared papers)Byeongho Heo (1 shared paper)Gibeom Park (1 shared paper)Seungjae Shin (1 shared paper)Dae‐Hyun Nam (2 shared papers)Teakgyu Hong (2 shared papers)Wonseok Hwang (2 shared papers)Kyungwoo Song (3 shared papers)
- Journals
- Proceedings of the AAAI Conference on Artificial Intelligence (5 papers)Journal of Korean Institute of Industrial Engineers (1 paper)
- Partner nations
- South KoreaCanadaUnited States
In The Last Decade
Mingi Ji
11 papers receiving 343 citations
Peers
Comparison fields: 5 of 71
- Computer Vision and Pattern Recognition 197
- Artificial Intelligence 198
- Signal Processing 27
- Information Systems 49
- Media Technology 19
Countries citing papers authored by Mingi Ji
This map shows the geographic impact of Mingi Ji'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 Mingi Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingi Ji more than expected).
Fields of papers citing papers by Mingi Ji
This network shows the impact of papers produced by Mingi Ji. 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 Mingi Ji. The network helps show where Mingi Ji may publish in the future.
Co-authors
The 11 scholars most cited alongside Mingi Ji, 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 | 120 | |
| 2 | 2021 | 101 | |
| 3 | 2022 | 59 | |
| 4 | 2019 | 23 | |
| 5 | 2020 | 21 | |
| 6 | BROS: A Pre-trained Language Model for Understanding Texts in Document | 2021 | 15 |
| 7 | 2018 | 8 | |
| 8 | 2019 | 2 | |
| 9 | 2024 | 1 | |
| 10 | 2022 | 1 | |
| 11 | 2023 | 1 |
About Mingi Ji
Mingi Ji is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Civil and Structural Engineering and Control and Systems Engineering, having authored 11 papers that have together received 352 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Recommender Systems and Techniques (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Multimodal Machine Learning Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers), Handwritten Text Recognition Techniques (2 papers), Advanced Neural Network Applications (2 papers) and Infrastructure Maintenance and Monitoring (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (197 citations), Artificial Intelligence (198 citations), Signal Processing (27 citations), Information Systems (49 citations) and Media Technology (19 citations). Mingi Ji has collaborated with scholars based in South Korea, Canada and United States. Frequent co-authors include Il‐Chul Moon, Byeongho Heo, Gibeom Park, Seungjae Shin, Dae‐Hyun Nam, Teakgyu Hong, Wonseok Hwang, Kyungwoo Song, Jinkyoo Park and Sungeun Kim. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence and Journal of Korean Institute of Industrial Engineers.
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.