Jun Yu
Impact in
- Computer Vision and Pattern Recognition top 0.05%
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
- Face and Expression Recognition
- Image Retrieval and Classification Techniques
- Advanced Neural Network Applications
- Media Technology top 0.2%
Papers in
-
- Advanced Image and Video Retrieval Techniques 89
- Multimodal Machine Learning Applications 67
- Human Pose and Action Recognition 47
- Image Retrieval and Classification Techniques 37
- Video Surveillance and Tracking Methods 28
- Video Analysis and Summarization 27
- Advanced Vision and Imaging 24
-
- Domain Adaptation and Few-Shot Learning 41
- Co-authors
- Dacheng Tao (33 shared papers)Yu Zhou (29 shared papers)Yong Rui (8 shared papers)Meng Wang (15 shared papers)Jianping Fan (22 shared papers)Chaoqun Hong (13 shared papers)Jian Zhang (12 shared papers)Qingming Huang (20 shared papers)
- Journals
- Neurocomputing (24 papers)IEEE Transactions on Circuits and Systems for Video Technology (16 papers)IEEE Transactions on Image Processing (15 papers)IEEE Transactions on Multimedia (14 papers)ACM Transactions on Multimedia Computing Communications and Applications (9 papers)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Jun Yu
261 papers receiving 9.6k citations
Jun Yu's Hit Papers
Peers
Comparison fields: 5 of 165
- Computer Vision and Pattern Recognition 6.9k
- Media Technology 960
- Artificial Intelligence 3.5k
- Computational Mathematics 43
- Signal Processing 561
Countries citing papers authored by Jun Yu
This map shows the geographic impact of Jun Yu'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 Jun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Yu more than expected).
Fields of papers citing papers by Jun Yu
This network shows the impact of papers produced by Jun Yu. 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 Jun Yu. The network helps show where Jun Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Yu, 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 282 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Multimodal Deep Autoencoder for Human Pose Recovery Hit paper breakdown → | 2015 | 502 |
| 2 | Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering Hit paper breakdown → | 2017 | 475 |
| 3 | Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition Hit paper breakdown → | 2019 | 465 |
| 4 | Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering Hit paper breakdown → | 2018 | 392 |
| 5 | Learning to Rank Using User Clicks and Visual Features for Image Retrieval Hit paper breakdown → | 2014 | 390 |
| 6 | Multimodal Transformer With Multi-View Visual Representation for Image Captioning Hit paper breakdown → | 2019 | 359 |
| 7 | Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking Hit paper breakdown → | 2016 | 334 |
| 8 | Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition Hit paper breakdown → | 2019 | 295 |
| 9 | Multimodal Face-Pose Estimation With Multitask Manifold Deep Learning Hit paper breakdown → | 2018 | 270 |
| 10 | iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning Hit paper breakdown → | 2016 | 266 |
| 11 | 2014 | 236 | |
| 12 | 2014 | 204 | |
| 13 | A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT Hit paper breakdown → | 2024 | 199 |
| 14 | 2018 | 184 | |
| 15 | 2018 | 172 | |
| 16 | 2015 | 169 | |
| 17 | 2014 | 167 | |
| 18 | 2017 | 156 | |
| 19 | 2013 | 149 | |
| 20 | 2019 | 142 |
About Jun Yu
Jun Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Computational Mechanics and Biomedical Engineering, having authored 282 papers that have together received 9.8k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (89 papers), Multimodal Machine Learning Applications (67 papers), Human Pose and Action Recognition (47 papers), Domain Adaptation and Few-Shot Learning (41 papers), Image Retrieval and Classification Techniques (37 papers), Video Surveillance and Tracking Methods (28 papers), Video Analysis and Summarization (27 papers) and Advanced Vision and Imaging (24 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.9k citations), Media Technology (960 citations), Artificial Intelligence (3.5k citations), Computational Mathematics (43 citations) and Signal Processing (561 citations). Jun Yu has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Dacheng Tao, Yu Zhou, Yong Rui, Meng Wang, Jianping Fan, Chaoqun Hong, Jian Zhang, Qingming Huang, Fei Gao and Min Tan. Their work appears in journals such as Neurocomputing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Image Processing, IEEE Transactions on Multimedia and ACM Transactions on Multimedia Computing Communications and Applications.
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.