Yining Hong
Impact in
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- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Intelligent Tutoring Systems and Adaptive Learning
Papers in
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- Multimodal Machine Learning Applications 4
- Human Pose and Action Recognition 2
- Advanced Image and Video Retrieval Techniques 1
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- Topic Modeling 4
- Natural Language Processing Techniques 3
- Artificial Intelligence in Games 1
- Domain Adaptation and Few-Shot Learning 1
- Intelligent Tutoring Systems and Adaptive Learning 1
- Co-authors
- Song‐Chun Zhu (3 shared papers)Siyuan Huang (3 shared papers)Qing Li (2 shared papers)Chuang Gan (4 shared papers)Xiaodan Liang (1 shared paper)Jianheng Tang (1 shared paper)Liang Lin (1 shared paper)Jinghui Qin (1 shared paper)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (4 papers)
- Partner nations
- United StatesChinaIceland
In The Last Decade
Yining Hong
10 papers receiving 149 citations
Peers
Comparison fields: 5 of 32
- Computer Vision and Pattern Recognition 67
- Artificial Intelligence 96
- Computational Mathematics 1
- Computer Graphics and Computer-Aided Design 3
- Geology 4
Countries citing papers authored by Yining Hong
This map shows the geographic impact of Yining Hong'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 Yining Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yining Hong more than expected).
Fields of papers citing papers by Yining Hong
This network shows the impact of papers produced by Yining Hong. 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 Yining Hong. The network helps show where Yining Hong may publish in the future.
Co-authors
The 25 scholars most cited alongside Yining Hong, 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 | 35 | |
| 2 | 2021 | 30 | |
| 3 | 2021 | 23 | |
| 4 | 2023 | 19 | |
| 5 | 2021 | 13 | |
| 6 | 2024 | 9 | |
| 7 | 2024 | 9 | |
| 8 | 2019 | 6 | |
| 9 | 2022 | 4 | |
| 10 | 2023 | 3 | |
| 11 | 2022 | 0 |
About Yining Hong
Yining Hong is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Information Systems and Computational Mechanics, having authored 11 papers that have together received 151 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Multimodal Machine Learning Applications (4 papers), Natural Language Processing Techniques (3 papers), Human Pose and Action Recognition (2 papers), Artificial Intelligence in Games (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper) and Advanced Image and Video Retrieval Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (67 citations), Artificial Intelligence (96 citations), Computational Mathematics (1 citation), Computer Graphics and Computer-Aided Design (3 citations) and Geology (4 citations). Yining Hong has collaborated with scholars based in United States, China and Iceland. Frequent co-authors include Song‐Chun Zhu, Siyuan Huang, Qing Li, Chuang Gan, Xiaodan Liang, Jianheng Tang, Liang Lin, Jinghui Qin, Joshua B. Tenenbaum and Chunru Lin. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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.