Zhongqin Wu
-
- Multimodal Machine Learning Applications 4
- Advanced Image Processing Techniques 3
- Image and Signal Denoising Methods 2
- Media Technology top 5%
- Artificial Intelligence top 10%
- Natural Language Processing Techniques 6
- Topic Modeling 5
- Speech Recognition and Synthesis 5
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- Speech and Audio Processing 4
- Music and Audio Processing 3
- Co-authors
- Xiao LiuPeng HuBoyun LiJiancheng LvXi PengZhilong JiZitao LiuXilin Chen
- Cited by
- Computer Vision and Pattern RecognitionMedia TechnologyComputer Graphics and Computer-Aided Design
- Journals
- IEEE/ACM Transactions on Audio Speech and Language Processing (1 paper)Neurocomputing (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Zhongqin Wu
20 papers receiving 369 citations
Hit Papers
Peers
Comparison fields: 5 of 60
- Computer Vision and Pattern Recognition 274
- Media Technology 75
- Computer Graphics and Computer-Aided Design 12
- Artificial Intelligence 103
- Signal Processing 31
Countries citing papers authored by Zhongqin Wu
This map shows the geographic impact of Zhongqin Wu'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 Zhongqin Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhongqin Wu more than expected).
Fields of papers citing papers by Zhongqin Wu
This network shows the impact of papers produced by Zhongqin Wu. 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 Zhongqin Wu. The network helps show where Zhongqin Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Zhongqin Wu, 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 | 2024 | 0 | |
| 2 | 2022 | 7 | |
| 3 | All-In-One Image Restoration for Unknown Corruptionbreakdown → | 2022 | 197 |
| 4 | 2022 | 16 | |
| 5 | 2022 | 3 | |
| 6 | 2022 | 25 | |
| 7 | 2021 | 8 | |
| 8 | 2021 | 13 | |
| 9 | 2021 | 29 | |
| 10 | 2021 | 4 | |
| 11 | 2021 | 5 | |
| 12 | 2021 | 5 | |
| 13 | 2021 | 3 | |
| 14 | 2021 | 13 | |
| 15 | 2021 | 8 | |
| 16 | 2021 | 19 | |
| 17 | 2021 | 8 | |
| 18 | 2020 | 5 | |
| 19 | 2020 | 7 | |
| 20 | 2015 | 1 |
About Zhongqin Wu
Zhongqin Wu is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 21 papers that have together received 377 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (5 papers), Speech Recognition and Synthesis (5 papers), Speech and Audio Processing (4 papers), Multimodal Machine Learning Applications (4 papers), Music and Audio Processing (3 papers), Advanced Image Processing Techniques (3 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (274 citations), Media Technology (75 citations) and Computer Graphics and Computer-Aided Design (12 citations). Zhongqin Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xiao Liu, Peng Hu, Boyun Li, Jiancheng Lv, Xi Peng, Zhilong Ji, Zitao Liu, Xilin Chen, Xiang Bai and Hui Liu. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Neurocomputing, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Chin J Endemiol.
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.