Tengda Han
Impact in
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- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Video Analysis and Summarization
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- Music and Audio Processing
- Speech and Audio Processing
Papers in
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- Human Pose and Action Recognition 4
- Multimodal Machine Learning Applications 3
- Advanced Data Compression Techniques 1
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- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Andrew Zisserman (4 shared papers)Max Bain (3 shared papers)Jaesung Huh (1 shared paper)Weidi Xie (4 shared papers)Stephen Jay Gould (1 shared paper)Anoop Cherian (1 shared paper)Sam Toyer (1 shared paper)Arsha Nagrani (2 shared papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)Neural Information Processing Systems (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (1 paper)
- Partner nations
- United KingdomChinaAustralia
In The Last Decade
Tengda Han
7 papers receiving 186 citations
Tengda Han's Hit Papers
Peers
Comparison fields: 5 of 45
- Computer Vision and Pattern Recognition 89
- Signal Processing 38
- Artificial Intelligence 86
- Language and Linguistics 13
- Human-Computer Interaction 6
Countries citing papers authored by Tengda Han
This map shows the geographic impact of Tengda Han'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 Tengda Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tengda Han more than expected).
Fields of papers citing papers by Tengda Han
This network shows the impact of papers produced by Tengda Han. 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 Tengda Han. The network helps show where Tengda Han may publish in the future.
Co-authors
The 16 scholars most cited alongside Tengda Han, 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 | WhisperX: Time-Accurate Speech Transcription of Long-Form Audio Hit paper breakdown → | 2023 | 98 |
| 2 | 2022 | 42 | |
| 3 | 2017 | 20 | |
| 4 | 2023 | 16 | |
| 5 | 2024 | 8 | |
| 6 | Self-supervised Co-Training for Video Representation Learning | 2020 | 5 |
| 7 | 2022 | 2 | |
| 8 | 2025 | 0 |
About Tengda Han
Tengda Han is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Civil and Structural Engineering and Control and Systems Engineering, having authored 8 papers that have together received 191 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (4 papers), Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Infrastructure Maintenance and Monitoring (1 paper), Video Coding and Compression Technologies (1 paper), Human Motion and Animation (1 paper), Music and Audio Processing (1 paper) and Advanced Data Compression Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (89 citations), Signal Processing (38 citations), Artificial Intelligence (86 citations), Language and Linguistics (13 citations) and Human-Computer Interaction (6 citations). Tengda Han has collaborated with scholars based in United Kingdom, China and Australia. Frequent co-authors include Andrew Zisserman, Max Bain, Jaesung Huh, Weidi Xie, Stephen Jay Gould, Anoop Cherian, Sam Toyer, Arsha Nagrani, Gül Varol and Zhe Chen. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Neural Information Processing Systems and HAL (Le Centre pour la Communication Scientifique Directe).
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.