Shangling Jui
Impact in
-
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Medical Image Segmentation Techniques
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Machine Learning and Data Classification
Papers in
-
- Multimodal Machine Learning Applications 6
- Image and Video Quality Assessment 4
- Advanced Neural Network Applications 3
- Medical Image Segmentation Techniques 2
- Image and Signal Denoising Methods 2
-
- Domain Adaptation and Few-Shot Learning 8
- Co-authors
- Joost van de Weijer (7 shared papers)Luis Herranz (7 shared papers)Xialei Liu (3 shared papers)Kai Wang (2 shared papers)Yongmei Cheng (1 shared paper)Lu Yu (1 shared paper)Bartłomiej Twardowski (1 shared paper)Chenshen Wu (2 shared papers)
In The Last Decade
Shangling Jui
23 papers receiving 456 citations
Peers
Comparison fields: 5 of 62
- Computer Vision and Pattern Recognition 282
- Artificial Intelligence 348
- Neurology 38
- Signal Processing 29
- Media Technology 14
Countries citing papers authored by Shangling Jui
This map shows the geographic impact of Shangling Jui'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 Shangling Jui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shangling Jui more than expected).
Fields of papers citing papers by Shangling Jui
This network shows the impact of papers produced by Shangling Jui. 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 Shangling Jui. The network helps show where Shangling Jui may publish in the future.
Co-authors
The 25 scholars most cited alongside Shangling Jui, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 183 | |
| 2 | 2020 | 67 | |
| 3 | 2021 | 52 | |
| 4 | 2015 | 38 | |
| 5 | 2020 | 30 | |
| 6 | 2023 | 25 | |
| 7 | 2024 | 9 | |
| 8 | 2023 | 9 | |
| 9 | 2014 | 7 | |
| 10 | 2021 | 7 | |
| 11 | 2015 | 6 | |
| 12 | 2025 | 6 | |
| 13 | 2022 | 6 | |
| 14 | 2023 | 4 | |
| 15 | 2025 | 4 | |
| 16 | 2025 | 3 | |
| 17 | 2023 | 2 | |
| 18 | 2023 | 2 | |
| 19 | 2022 | 2 | |
| 20 | 2022 | 2 |
About Shangling Jui
Shangling Jui is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Electrical and Electronic Engineering and Hardware and Architecture, having authored 23 papers that have together received 467 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (6 papers), Advanced Image Fusion Techniques (4 papers), Image and Video Quality Assessment (4 papers), Advanced Neural Network Applications (3 papers), Advanced Bandit Algorithms Research (2 papers), Medical Image Segmentation Techniques (2 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (282 citations), Artificial Intelligence (348 citations), Neurology (38 citations), Signal Processing (29 citations) and Media Technology (14 citations). Shangling Jui has collaborated with scholars based in China, Canada and Spain. Frequent co-authors include Joost van de Weijer, Luis Herranz, Xialei Liu, Kai Wang, Yongmei Cheng, Lu Yu, Bartłomiej Twardowski, Chenshen Wu, Andrew D. Bagdanov and Bogdan Raducanu. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, International Journal of Computer Vision, International Journal of Computational Intelligence Systems, IEEE Intelligent Systems and IEEE Access.
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.