Ting Yao
- Computer Vision and Pattern Recognition top 0.05%
- Multimodal Machine Learning Applications 83
- Advanced Image and Video Retrieval Techniques 53
- Human Pose and Action Recognition 52
- Video Analysis and Summarization 35
- Advanced Neural Network Applications 19
- Advanced Vision and Imaging 15
- Artificial Intelligence top 0.2%
- Domain Adaptation and Few-Shot Learning 45
- Topic Modeling 14
- Human-Computer Interaction top 1%
- Signal Processing top 2%
- Media Technology top 1%
Ting Yao
199 papers receiving 9.3k citations
Hit Papers
Peers
Comparison fields: 5 of 176
- Computer Vision and Pattern Recognition 7.4k
- Artificial Intelligence 3.4k
- Human-Computer Interaction 365
- Signal Processing 380
- Media Technology 300
Countries citing papers authored by Ting Yao
This map shows the geographic impact of Ting Yao'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 Ting Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ting Yao more than expected).
Fields of papers citing papers by Ting Yao
This network shows the impact of papers produced by Ting Yao. 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 Ting Yao. The network helps show where Ting Yao may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ting Yao, 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 | 2025 | 4 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 2 | |
| 4 | 2025 | 3 | |
| 5 | 2023 | 28 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 9 | |
| 9 | 2023 | 22 | |
| 10 | 2023 | 6 | |
| 11 | Adjacent Copper Single Atoms Promote C–C Coupling in Electrochemical CO2 Reduction for the Efficient Conversion of Ethanolbreakdown → | 2023 | 218 |
| 12 | 2023 | 10 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 2 | |
| 15 | 2023 | 8 | |
| 16 | 2022 | 41 | |
| 17 | 2019 | 11 | |
| 18 | VireoJD-MM @ TRECVid 2019: Activities in Extended Video (ActEV). | 2019 | 1 |
| 19 | 2019 | 24 | |
| 20 | Learning Spatio-Temporal Representation with Pseudo-3D Residual Networksbreakdown → | 2017 | 1194 |
About Ting Yao
Ting Yao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Catalysis and Discrete Mathematics and Combinatorics, having authored 221 papers that have together received 9.6k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (83 papers), Advanced Image and Video Retrieval Techniques (53 papers), Human Pose and Action Recognition (52 papers), Domain Adaptation and Few-Shot Learning (45 papers), Video Analysis and Summarization (35 papers), Advanced Neural Network Applications (19 papers), Advanced Vision and Imaging (15 papers) and Topic Modeling (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (7.4k citations), Artificial Intelligence (3.4k citations), Human-Computer Interaction (365 citations), Signal Processing (380 citations) and Media Technology (300 citations). Ting Yao has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Tao Mei, Yingwei Pan, Zhaofan Qiu, Yong Rui, Jun Xu, Yehao Li, Houqiang Li, Chong‐Wah Ngo, Tao Mei and Xinmei Tian. Their work appears in journals such as IEEE Transactions on Multimedia, ACM Transactions on Multimedia Computing Communications and Applications, IEEE Transactions on Image Processing, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Neural Networks and Learning Systems.
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.