Cong Yao
Impact in
- Computer Vision and Pattern Recognition top 0.05%
- Handwritten Text Recognition Techniques
- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Multimodal Machine Learning Applications
- Media Technology top 0.5%
- Vehicle License Plate Recognition
Papers in
-
- Handwritten Text Recognition Techniques 45
- Medical Image Segmentation Techniques 5
- Generative Adversarial Networks and Image Synthesis 4
- Advanced Neural Network Applications 4
- Advanced Image and Video Retrieval Techniques 3
- Image Retrieval and Classification Techniques 3
- Video Surveillance and Tracking Methods 2
- Human Pose and Action Recognition 2
- Co-authors
- Xiang Bai (35 shared papers)Baoguang Shi (8 shared papers)Pengyuan Lyu (7 shared papers)Wenyu Liu (11 shared papers)Minghui Liao (9 shared papers)Xinggang Wang (9 shared papers)Wenhao Wu (4 shared papers)Zhaoyi Wan (6 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (4 papers)Pattern Recognition (4 papers)IEEE Transactions on Image Processing (3 papers)International Journal of Computer Vision (2 papers)Lecture notes in computer science (10 papers)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Cong Yao
63 papers receiving 11k citations
Cong Yao's Hit Papers
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 10.2k
- Media Technology 433
- Artificial Intelligence 487
- Industrial and Manufacturing Engineering 112
- Signal Processing 60
Countries citing papers authored by Cong Yao
This map shows the geographic impact of Cong 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 Cong Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cong Yao more than expected).
Fields of papers citing papers by Cong Yao
This network shows the impact of papers produced by Cong 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 Cong Yao. The network helps show where Cong Yao may publish in the future.
Co-authors
The 25 scholars most cited alongside Cong 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
Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition Hit paper breakdown → | 2016 | 2241 |
| 2 | EAST: An Efficient and Accurate Scene Text Detector Hit paper breakdown → | 2017 | 1337 |
| 3 | ASTER: An Attentional Scene Text Recognizer with Flexible Rectification Hit paper breakdown → | 2018 | 646 |
| 4 | Detecting texts of arbitrary orientations in natural images Hit paper breakdown → | 2012 | 591 |
| 5 | Real-Time Scene Text Detection with Differentiable Binarization Hit paper breakdown → | 2020 | 555 |
| 6 | Robust Scene Text Recognition with Automatic Rectification Hit paper breakdown → | 2016 | 527 |
| 7 | Multi-oriented Text Detection with Fully Convolutional Networks Hit paper breakdown → | 2016 | 467 |
| 8 | TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes Hit paper breakdown → | 2018 | 454 |
| 9 | 2018 | 401 | |
| 10 | Scene Text Detection and Recognition: The Deep Learning Era Hit paper breakdown → | 2020 | 339 |
| 11 | 2015 | 316 | |
| 12 | 2018 | 307 | |
| 13 | 2014 | 270 | |
| 14 | Real-Time Scene Text Detection With Differentiable Binarization and Adaptive Scale Fusion Hit paper breakdown → | 2022 | 256 |
| 15 | 2014 | 239 | |
| 16 | 2015 | 223 | |
| 17 | 2019 | 200 | |
| 18 | 2019 | 190 | |
| 19 | 2019 | 182 | |
| 20 | 2017 | 167 |
About Cong Yao
Cong Yao is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Information Systems, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 65 papers that have together received 11.4k indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (45 papers), Medical Image Segmentation Techniques (5 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Advanced Neural Network Applications (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Image Retrieval and Classification Techniques (3 papers), Video Surveillance and Tracking Methods (2 papers) and Human Pose and Action Recognition (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (10.2k citations), Media Technology (433 citations), Artificial Intelligence (487 citations), Industrial and Manufacturing Engineering (112 citations) and Signal Processing (60 citations). Cong Yao has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Xiang Bai, Baoguang Shi, Pengyuan Lyu, Wenyu Liu, Minghui Liao, Xinggang Wang, Wenhao Wu, Zhaoyi Wan, Jiajun Liang and Xin James He. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, IEEE Transactions on Image Processing, International Journal of Computer Vision and Lecture notes in computer science.
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.