Min Jin Chong
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Media Technology
- Computer Graphics and Computer-Aided Design top 10%
- Computational Mechanics
- Co-authors
- David ForsythJiajun LuAditya DeshpandeKaizhao LiangJingen LiuBo LiWen–Sheng ChuAbhishek Kumar
- Topics
- Generative Adversarial Networks and Image Synthesis (3 papers)Face recognition and analysis (2 papers)Advanced Neural Network Applications (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignMedia Technology
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)arXiv (Cornell University)
- Partner nations
- United StatesJamaicaHong Kong
In The Last Decade
Min Jin Chong
8 papers receiving 182 citations
Peers
Comparison fields: 5 of 47
- Computer Vision and Pattern Recognition 152
- Artificial Intelligence 42
- Media Technology 21
- Computer Graphics and Computer-Aided Design 18
- Computational Mechanics 13
Countries citing papers authored by Min Jin Chong
This map shows the geographic impact of Min Jin Chong'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 Min Jin Chong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min Jin Chong more than expected).
Fields of papers citing papers by Min Jin Chong
This network shows the impact of papers produced by Min Jin Chong. 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 Min Jin Chong. The network helps show where Min Jin Chong may publish in the future.
Co-authorship network of co-authors of Min Jin Chong
This figure shows the co-authorship network connecting the top 25 collaborators of Min Jin Chong. A scholar is included among the top collaborators of Min Jin Chong 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 Min Jin Chong. Min Jin Chong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 15 | |
| 4 | 22 | |
| 5 | Big but Imperceptible Adversarial Perturbations via Semantic Manipulation. | 8 |
| 6 | 24 | |
| 7 | EEG-GRAPH: A factor-graph-based model for capturing spatial, temporal, and observational relationships in electroencephalograms | 9 |
| 8 | 112 |
About Min Jin Chong
Min Jin Chong is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Signal Processing, having authored 8 papers that have together received 192 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Face recognition and analysis (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (152 citations), Computer Graphics and Computer-Aided Design (18 citations) and Media Technology (21 citations). Min Jin Chong has collaborated with scholars based in United States, Jamaica and Hong Kong. Frequent co-authors include David Forsyth, Jiajun Lu, Aditya Deshpande, Kaizhao Liang, Jingen Liu, Bo Li, Wen–Sheng Chu, Abhishek Kumar, Bo Li and Yogatheesan Varatharajah. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and arXiv (Cornell University).
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.