Chengzhi Mao
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning 6
- Domain Adaptation and Few-Shot Learning 4
- Anomaly Detection Techniques and Applications 2
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- Generative Adversarial Networks and Image Synthesis 2
- Advanced Neural Network Applications 2
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- Advanced Malware Detection Techniques 1
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- Neural dynamics and brain function 1
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- Indoor and Outdoor Localization Technologies 1
- Co-authors
- Carl VondrickJunfeng YangHao WangTiancheng YuYuan ShenBaishakhi RaySachit MenonJames Z. Wang
- Journals
- Electronics (1 paper)Symmetry (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- United StatesNetherlandsChina
In The Last Decade
Chengzhi Mao
15 papers receiving 166 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 104
- Computer Vision and Pattern Recognition 64
- Signal Processing 25
- Health Informatics 2
- Hardware and Architecture 4
Countries citing papers authored by Chengzhi Mao
This map shows the geographic impact of Chengzhi Mao'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 Chengzhi Mao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chengzhi Mao more than expected).
Fields of papers citing papers by Chengzhi Mao
This network shows the impact of papers produced by Chengzhi Mao. 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 Chengzhi Mao. The network helps show where Chengzhi Mao may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Chengzhi Mao, 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 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 5 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 16 | |
| 6 | 2023 | 2 | |
| 7 | 2022 | 2 | |
| 8 | 2022 | 21 | |
| 9 | 2021 | 21 | |
| 10 | 2021 | 32 | |
| 11 | 2020 | 13 | |
| 12 | Unrestricted Adversarial Attacks For Semantic Segmentation | 2019 | 1 |
| 13 | 2019 | 10 | |
| 14 | 2019 | 13 | |
| 15 | 2018 | 29 | |
| 16 | 2016 | 2 |
About Chengzhi Mao
Chengzhi Mao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 16 papers that have together received 171 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (6 papers), Domain Adaptation and Few-Shot Learning (4 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Neural Network Applications (2 papers), Anomaly Detection Techniques and Applications (2 papers), Advanced Malware Detection Techniques (1 paper), Neural dynamics and brain function (1 paper) and Indoor and Outdoor Localization Technologies (1 paper). The work is most often cited by research in Artificial Intelligence (104 citations), Computer Vision and Pattern Recognition (64 citations) and Signal Processing (25 citations). Chengzhi Mao has collaborated with scholars based in United States, Netherlands and China. Frequent co-authors include Carl Vondrick, Junfeng Yang, Hao Wang, Tiancheng Yu, Yuan Shen, Baishakhi Ray, Sachit Menon, James Z. Wang, Hao Wang and Elias Bareinboim. Their work appears in journals such as Electronics, Symmetry and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.