Zhourong Chen
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Human-Computer Interaction
- Signal Processing
- Topics
- Generative Adversarial Networks and Image Synthesis (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)Topic Modeling (2 papers)
- Journals
- Artificial IntelligenceRare & Special e-Zone (The Hong Kong University of Science and Technology)CHI Conference on Human Factors in Computing Systems
- Partner nations
- Hong KongUnited StatesCanada
In The Last Decade
Zhourong Chen
9 papers receiving 161 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 87
- Computer Vision and Pattern Recognition 82
- Information Systems 31
- Human-Computer Interaction 19
- Signal Processing 18
Countries citing papers authored by Zhourong Chen
This map shows the geographic impact of Zhourong Chen'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 Zhourong Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhourong Chen more than expected).
Fields of papers citing papers by Zhourong Chen
This network shows the impact of papers produced by Zhourong Chen. 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 Zhourong Chen. The network helps show where Zhourong Chen may publish in the future.
Co-authorship network of co-authors of Zhourong Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Zhourong Chen. A scholar is included among the top collaborators of Zhourong Chen 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 Zhourong Chen. Zhourong Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | 39 | |
| 3 | 54 | |
| 4 | GaterNet: Dynamic Filter Selection in Convolutional Neural Network via a Dedicated Global Gating Network. | 6 |
| 5 | Latent Tree Variational Autoencoder for Joint Representation Learning and Multidimensional Clustering. | 1 |
| 6 | 12 | |
| 7 | 23 | |
| 8 | 6 | |
| 9 | 9 |
About Zhourong Chen
Zhourong Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 9 papers that have together received 164 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Topic Modeling (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (82 citations), Human-Computer Interaction (19 citations) and Artificial Intelligence (87 citations). Zhourong Chen has collaborated with scholars based in Hong Kong, United States and Canada. Frequent co-authors include Samy Bengio, Yang Li, Si Si, Leonard K. M. Poon, Nevin L. Zhang, Gang Li, Peixian Chen, Tovi Grossman, Yang Li and Bryan Wang. Their work appears in journals such as Artificial Intelligence, Rare & Special e-Zone (The Hong Kong University of Science and Technology) and CHI Conference on Human Factors in Computing 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.