Dengxin Dai
- Computer Vision and Pattern Recognition top 0.2%
- Artificial Intelligence top 0.5%
- Media Technology top 0.5%
- Aerospace Engineering top 2%
- Environmental Engineering top 5%
- Co-authors
- Luc Van GoolChristos SakaridisLukas HoyerWen YangSimon VandenhendeStamatios GeorgoulisMarc ProesmansWouter Van Gansbeke
- Topics
- Domain Adaptation and Few-Shot Learning (25 papers)Advanced Neural Network Applications (23 papers)Advanced Image and Video Retrieval Techniques (21 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image ProcessingInternational Journal of Computer Vision
- Partner nations
- SwitzerlandBelgiumChina
In The Last Decade
Dengxin Dai
85 papers receiving 4.7k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Computer Vision and Pattern Recognition 3.3k
- Artificial Intelligence 1.8k
- Media Technology 822
- Aerospace Engineering 495
- Environmental Engineering 316
Countries citing papers authored by Dengxin Dai
This map shows the geographic impact of Dengxin Dai'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 Dengxin Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dengxin Dai more than expected).
Fields of papers citing papers by Dengxin Dai
This network shows the impact of papers produced by Dengxin Dai. 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 Dengxin Dai. The network helps show where Dengxin Dai may publish in the future.
Co-authorship network of co-authors of Dengxin Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Dengxin Dai. A scholar is included among the top collaborators of Dengxin Dai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dengxin Dai. Dengxin Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 51 | |
| 5 | 4 | |
| 6 | 2 | |
| 7 | 7 | |
| 8 | 2 | |
| 9 | 0 | |
| 10 | 2 | |
| 11 | Deep Gradient Learning for Efficient Camouflaged Object Detectionbreakdown → | 132 |
| 12 | 16 | |
| 13 | 8 | |
| 14 | 14 | |
| 15 | 3 | |
| 16 | 83 | |
| 17 | 51 | |
| 18 | Semantic Foggy Scene Understanding with Synthetic Databreakdown → | 751 |
| 19 | How Useful Is Image Super-resolution to Other Vision Tasks? | 1 |
| 20 | 29 |
About Dengxin Dai
Dengxin Dai is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computational Mathematics, having authored 91 papers that have together received 4.8k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (25 papers), Advanced Neural Network Applications (23 papers) and Advanced Image and Video Retrieval Techniques (21 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.3k citations), Media Technology (822 citations) and Artificial Intelligence (1.8k citations). Dengxin Dai has collaborated with scholars based in Switzerland, Belgium and China. Frequent co-authors include Luc Van Gool, Christos Sakaridis, Lukas Hoyer, Wen Yang, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Wouter Van Gansbeke, Olga Fink and Qin Wang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and International Journal of Computer Vision.
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.