Changjie Fan
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- Face recognition and analysis 17
- Generative Adversarial Networks and Image Synthesis 15
- Signal Processing top 2%
- Artificial Intelligence top 2%
- Reinforcement Learning in Robotics 21
- Artificial Intelligence in Games 17
- Natural Language Processing Techniques 8
- Software top 5%
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- Human Motion and Animation 10
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- Recommender Systems and Techniques 9
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- Sports Analytics and Performance 8
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)ACM Transactions on Graphics (1 paper)Neurocomputing (1 paper)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Changjie Fan
92 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 96
- Computer Vision and Pattern Recognition 696
- Signal Processing 266
- Artificial Intelligence 593
- Software 63
- Computer Graphics and Computer-Aided Design 41
Countries citing papers authored by Changjie Fan
This map shows the geographic impact of Changjie Fan'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 Changjie Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Changjie Fan more than expected).
Fields of papers citing papers by Changjie Fan
This network shows the impact of papers produced by Changjie Fan. 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 Changjie Fan. The network helps show where Changjie Fan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Changjie Fan, 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 | 1 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 0 | |
| 8 | 2023 | 4 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 23 | |
| 12 | 2021 | 9 | |
| 13 | Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games | 2021 | 6 |
| 14 | 2021 | 14 | |
| 15 | 2021 | 30 | |
| 16 | 2020 | 1 | |
| 17 | Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping | 2020 | 2 |
| 18 | 2020 | 12 | |
| 19 | Hierarchical Deep Multiagent Reinforcement Learning | 2018 | 9 |
| 20 | A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents | 2018 | 33 |
About Changjie Fan
Changjie Fan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 98 papers that have together received 1.5k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (21 papers), Face recognition and analysis (17 papers), Artificial Intelligence in Games (17 papers), Generative Adversarial Networks and Image Synthesis (15 papers), Human Motion and Animation (10 papers), Recommender Systems and Techniques (9 papers), Natural Language Processing Techniques (8 papers) and Sports Analytics and Performance (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (696 citations), Signal Processing (266 citations) and Artificial Intelligence (593 citations). Changjie Fan has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Yu Ding, Lincheng Li, Zhimeng Zhang, Jianye Hao, Yingfeng Chen, Jianrong Tao, Xin Yu, Suzhen Wang, Runze Wu and Zhaopeng Meng. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and Neurocomputing.
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.