Min-Oh Heo
Impact in
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Advanced Neural Network Applications
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- Domain Adaptation and Few-Shot Learning
- Topic Modeling
Papers in
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- Domain Adaptation and Few-Shot Learning 1
- Machine Learning and Algorithms 1
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- Advanced biosensing and bioanalysis techniques 1
- Co-authors
- Byoung‐Tak Zhang (3 shared papers)Sang-Woo Lee (1 shared paper)Jung-Woo Ha (1 shared paper)Donghyun Kwak (1 shared paper)Jin-Hwa Kim (1 shared paper)Jeonghee Kim (1 shared paper)Sun Min Kim (1 shared paper)Sohee Lim (1 shared paper)
- Journals
- Measurement (1 paper)Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- South Korea
In The Last Decade
Min-Oh Heo
4 papers receiving 68 citations
Peers
Comparison fields: 5 of 22
- Computer Vision and Pattern Recognition 51
- Artificial Intelligence 44
- Transportation 5
- Signal Processing 4
- Computer Science Applications 2
Countries citing papers authored by Min-Oh Heo
This map shows the geographic impact of Min-Oh Heo'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 Min-Oh Heo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min-Oh Heo more than expected).
Fields of papers citing papers by Min-Oh Heo
This network shows the impact of papers produced by Min-Oh Heo. 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 Min-Oh Heo. The network helps show where Min-Oh Heo may publish in the future.
Co-authors
The 13 scholars most cited alongside Min-Oh Heo, 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 | Multimodal Residual Learning for Visual QA | 2016 | 55 |
| 2 | 2006 | 8 | |
| 3 | Real-time Route Inference and Learning for Smartphone Users using Probabilistic Graphical Models | 2012 | 6 |
| 4 | 2022 | 3 |
About Min-Oh Heo
Min-Oh Heo is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Signal Processing and Transportation, having authored 4 papers that have together received 72 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (1 paper), Machine Learning and Algorithms (1 paper), Advanced Image and Video Retrieval Techniques (1 paper), Data Management and Algorithms (1 paper), Radar Systems and Signal Processing (1 paper), Microwave Imaging and Scattering Analysis (1 paper), Geographic Information Systems Studies (1 paper) and Advanced biosensing and bioanalysis techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (51 citations), Artificial Intelligence (44 citations), Transportation (5 citations), Signal Processing (4 citations) and Computer Science Applications (2 citations). Min-Oh Heo has collaborated with scholars based in South Korea. Frequent co-authors include Byoung‐Tak Zhang, Sang-Woo Lee, Jung-Woo Ha, Donghyun Kwak, Jin-Hwa Kim, Jeonghee Kim, Sun Min Kim, Sohee Lim, Jaehoon Jung and Seong-Cheol Kim. Their work appears in journals such as Measurement, Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong 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.