Yu-Jhe Li
Impact in
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Face recognition and analysis
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
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- Domain Adaptation and Few-Shot Learning
Papers in
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- Video Surveillance and Tracking Methods 3
- Human Pose and Action Recognition 3
- Advanced Neural Network Applications 3
- Advanced Image and Video Retrieval Techniques 2
- Face recognition and analysis 1
- Co-authors
- Kris Kitani (9 shared papers)Xinshuo Weng (3 shared papers)Kan Chen (1 shared paper)BoRui Wu (1 shared paper)Zijian He (1 shared paper)Péter Vajda (1 shared paper)Yen‐Cheng Liu (1 shared paper)Xiaoliang Dai (1 shared paper)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Yu-Jhe Li
8 papers receiving 235 citations
Yu-Jhe Li's Hit Papers
Peers
Comparison fields: 5 of 45
- Computer Vision and Pattern Recognition 177
- Artificial Intelligence 90
- Media Technology 21
- Instrumentation 8
- Aerospace Engineering 48
Countries citing papers authored by Yu-Jhe Li
This map shows the geographic impact of Yu-Jhe Li'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 Yu-Jhe Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu-Jhe Li more than expected).
Fields of papers citing papers by Yu-Jhe Li
This network shows the impact of papers produced by Yu-Jhe Li. 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 Yu-Jhe Li. The network helps show where Yu-Jhe Li may publish in the future.
Co-authors
The 12 scholars most cited alongside Yu-Jhe Li, 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 | Cross-Domain Adaptive Teacher for Object Detection Hit paper breakdown → | 2022 | 139 |
| 2 | 2021 | 35 | |
| 3 | 2022 | 28 | |
| 4 | 2021 | 16 | |
| 5 | 2021 | 9 | |
| 6 | 2023 | 8 | |
| 7 | 2023 | 4 | |
| 8 | 2021 | 2 | |
| 9 | 2024 | 0 |
About Yu-Jhe Li
Yu-Jhe Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Automotive Engineering and Civil and Structural Engineering, having authored 9 papers that have together received 241 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (3 papers), Human Pose and Action Recognition (3 papers), Advanced Neural Network Applications (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Concrete Corrosion and Durability (1 paper), COVID-19 diagnosis using AI (1 paper), Robotics and Sensor-Based Localization (1 paper) and Face recognition and analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (177 citations), Artificial Intelligence (90 citations), Media Technology (21 citations), Instrumentation (8 citations) and Aerospace Engineering (48 citations). Yu-Jhe Li has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Kris Kitani, Xinshuo Weng, Kan Chen, BoRui Wu, Zijian He, Péter Vajda, Yen‐Cheng Liu, Xiaoliang Dai, Chih‐Yao Ma and Matthew O’Toole. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.