Jierun Chen
Impact in
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
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- Industrial Vision Systems and Defect Detection
Papers in
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- Advanced Vision and Imaging 1
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- 3D Shape Modeling and Analysis 1
- Sparse and Compressive Sensing Techniques 1
- Co-authors
- S.-H. Gary Chan (4 shared papers)Weipeng Zhuo (3 shared papers)Wen Song (1 shared paper)Hao He (2 shared papers)Chul‐Ho Lee (1 shared paper)Sangtae Ha (2 shared papers)Haoliang Li (1 shared paper)Ying-Cong Chen (1 shared paper)
- Journals
- Computer Graphics Forum (1 paper)Rare & Special e-Zone (The Hong Kong University of Science and Technology) (4 papers)
- Partner nations
- Hong KongUnited StatesNetherlands
In The Last Decade
Jierun Chen
5 papers receiving 1.2k citations
Jierun Chen's Hit Papers
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 607
- Industrial and Manufacturing Engineering 224
- Media Technology 130
- Safety, Risk, Reliability and Quality 64
- Analytical Chemistry 66
Countries citing papers authored by Jierun Chen
This map shows the geographic impact of Jierun Chen'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 Jierun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jierun Chen more than expected).
Fields of papers citing papers by Jierun Chen
This network shows the impact of papers produced by Jierun Chen. 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 Jierun Chen. The network helps show where Jierun Chen may publish in the future.
Co-authors
The 9 scholars most cited alongside Jierun Chen, 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 | Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks Hit paper breakdown → | 2023 | 1161 |
| 2 | 2023 | 5 | |
| 3 | 2023 | 4 | |
| 4 | 2024 | 2 | |
| 5 | 2023 | 1 |
About Jierun Chen
Jierun Chen is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Artificial Intelligence, Electrical and Electronic Engineering and Transportation, having authored 5 papers that have together received 1.2k indexed citations. Recurring topics across this work include Indoor and Outdoor Localization Technologies (2 papers), Computer Graphics and Visualization Techniques (1 paper), Mobile Crowdsensing and Crowdsourcing (1 paper), COVID-19 diagnosis using AI (1 paper), 3D Shape Modeling and Analysis (1 paper), Human Mobility and Location-Based Analysis (1 paper), Advanced Vision and Imaging (1 paper) and Sparse and Compressive Sensing Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (607 citations), Industrial and Manufacturing Engineering (224 citations), Media Technology (130 citations), Safety, Risk, Reliability and Quality (64 citations) and Analytical Chemistry (66 citations). Jierun Chen has collaborated with scholars based in Hong Kong, United States and Netherlands. Frequent co-authors include S.-H. Gary Chan, Weipeng Zhuo, Wen Song, Hao He, Chul‐Ho Lee, Chul‐Ho Lee, Sangtae Ha, Haoliang Li and Ying-Cong Chen. Their work appears in journals such as Computer Graphics Forum and Rare & Special e-Zone (The Hong Kong University of Science and Technology).
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.