Tengda Han
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Signal Processing
- Experimental and Cognitive Psychology
- Language and Linguistics
- Co-authors
- Andrew ZissermanMax BainWeidi XieJaesung HuhSam ToyerAnoop CherianStephen Jay GouldGül Varol
- Topics
- Human Pose and Action Recognition (4 papers)Multimodal Machine Learning Applications (3 papers)Domain Adaptation and Few-Shot Learning (2 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural Information Processing SystemsSPIRE - Sciences Po Institutional REpository
- Partner nations
- United KingdomChinaAustralia
In The Last Decade
Tengda Han
7 papers receiving 159 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 82
- Artificial Intelligence 80
- Signal Processing 35
- Experimental and Cognitive Psychology 11
- Language and Linguistics 11
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-authorship network of co-authors of Tengda Han
This figure shows the co-authorship network connecting the top 25 collaborators of Tengda Han. A scholar is included among the top collaborators of Tengda Han 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 Tengda Han. Tengda Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 73 | |
| 3 | 16 | |
| 4 | 39 | |
| 5 | 2 | |
| 6 | Self-supervised Co-Training for Video Representation Learning | 5 |
| 7 | 20 |
About Tengda Han
Tengda Han is a scholar working on Energy Engineering and Power Technology, Computer Vision and Pattern Recognition and Language and Linguistics, having authored 7 papers that have together received 163 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (4 papers), Multimodal Machine Learning Applications (3 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (82 citations), Signal Processing (35 citations) and Artificial Intelligence (80 citations). Tengda Han has collaborated with scholars based in United Kingdom, China and Australia. Frequent co-authors include Andrew Zisserman, Max Bain, Weidi Xie, Jaesung Huh, Sam Toyer, Anoop Cherian, Stephen Jay Gould, Gül Varol, Arsha Nagrani and Andrew Zisserman. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Neural Information Processing Systems and SPIRE - Sciences Po Institutional REpository.
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.