Erjin Zhou
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 2%
- Electrical and Electronic Engineering top 10%
- Biomedical Engineering top 10%
- Signal Processing top 5%
- Topics
- Human Pose and Action Recognition (5 papers)Face recognition and analysis (4 papers)Face and Expression Recognition (3 papers)
- Journals
- Pattern RecognitionIEEE Transactions on Neural Networks and Learning SystemsImage and Vision Computing
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Erjin Zhou
15 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 113
- Computer Vision and Pattern Recognition 1.9k
- Artificial Intelligence 689
- Electrical and Electronic Engineering 570
- Biomedical Engineering 258
- Signal Processing 144
Countries citing papers authored by Erjin Zhou
This map shows the geographic impact of Erjin Zhou'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 Erjin Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erjin Zhou more than expected).
Fields of papers citing papers by Erjin Zhou
This network shows the impact of papers produced by Erjin Zhou. 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 Erjin Zhou. The network helps show where Erjin Zhou may publish in the future.
Co-authorship network of co-authors of Erjin Zhou
This figure shows the co-authorship network connecting the top 25 collaborators of Erjin Zhou. A scholar is included among the top collaborators of Erjin Zhou 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 Erjin Zhou. Erjin Zhou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 52 | |
| 3 | 6 | |
| 4 | 153 | |
| 5 | 102 | |
| 6 | TokenPose: Learning Keypoint Tokens for Human Pose Estimationbreakdown → | 210 |
| 7 | 168 | |
| 8 | High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identificationbreakdown → | 321 |
| 9 | Symmetric Variational Autoencoder and Connections to Adversarial Learning | 12 |
| 10 | 12 | |
| 11 | Going Deeper with Embedded FPGA Platform for Convolutional Neural Networkbreakdown → | 882 |
| 12 | 87 | |
| 13 | 73 | |
| 14 | 46 | |
| 15 | 231 |
About Erjin Zhou
Erjin Zhou is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Artificial Intelligence, having authored 15 papers that have together received 2.4k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (5 papers), Face recognition and analysis (4 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.9k citations), Human-Computer Interaction (127 citations) and Artificial Intelligence (689 citations). Erjin Zhou has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Haoqiang Fan, Ningyi Xu, Huazhong Yang, Boxun Li, Zhicheng Wang, Jie Wang, Tianqi Tang, Jincheng Yu, Jiantao Qiu and Sen Song. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems and Image and Vision Computing.
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.