Yake Wei
Impact in
- Signal Processing top 10%
- Music and Audio Processing
- Speech and Audio Processing
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Video Analysis and Summarization
- Human Pose and Action Recognition
Papers in
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- Multimodal Machine Learning Applications 3
- Advanced Image and Video Retrieval Techniques 2
- Video Analysis and Summarization 2
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- Speech and Audio Processing 2
- Music and Audio Processing 2
- Co-authors
- Di Hu (8 shared papers)Dong Wang (1 shared paper)Xiaokang Peng (1 shared paper)Ji-Rong Wen (3 shared papers)Yapeng Tian (1 shared paper)Guangyao Li (1 shared paper)Chenliang Xu (1 shared paper)Han Zhang (1 shared paper)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)Pattern Recognition (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Yake Wei
7 papers receiving 265 citations
Yake Wei's Hit Papers
Peers
Comparison fields: 5 of 55
- Signal Processing 83
- Computer Vision and Pattern Recognition 140
- Computational Mathematics 3
- Artificial Intelligence 118
- Experimental and Cognitive Psychology 19
Countries citing papers authored by Yake Wei
This map shows the geographic impact of Yake Wei'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 Yake Wei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yake Wei more than expected).
Fields of papers citing papers by Yake Wei
This network shows the impact of papers produced by Yake Wei. 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 Yake Wei. The network helps show where Yake Wei may publish in the future.
Co-authors
The 17 scholars most cited alongside Yake Wei, 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 | Balanced Multimodal Learning via On-the-fly Gradient Modulation Hit paper breakdown → | 2022 | 154 |
| 2 | 2022 | 49 | |
| 3 | 2021 | 30 | |
| 4 | 2023 | 28 | |
| 5 | 2024 | 7 | |
| 6 | 2024 | 2 | |
| 7 | 2025 | 1 | |
| 8 | 2025 | 0 |
About Yake Wei
Yake Wei is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Infectious Diseases and Organic Chemistry, having authored 8 papers that have together received 271 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Speech and Audio Processing (2 papers), Video Analysis and Summarization (2 papers), Music and Audio Processing (2 papers), Speech and dialogue systems (1 paper), Natural Language Processing Techniques (1 paper) and Target Tracking and Data Fusion in Sensor Networks (1 paper). The work is most often cited by research in Signal Processing (83 citations), Computer Vision and Pattern Recognition (140 citations), Computational Mathematics (3 citations), Artificial Intelligence (118 citations) and Experimental and Cognitive Psychology (19 citations). Yake Wei has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Di Hu, Dong Wang, Xiaokang Peng, Ji-Rong Wen, Yapeng Tian, Guangyao Li, Chenliang Xu, Han Zhang, Zheng Wang and Rui Qian. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.