Chuang Gan

346 total papers · 14.7k total citations
114 papers, 5.4k citations indexed

About

Chuang Gan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Chuang Gan has authored 114 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Computer Vision and Pattern Recognition, 58 papers in Artificial Intelligence and 14 papers in Signal Processing. Recurrent topics in Chuang Gan's work include Multimodal Machine Learning Applications (43 papers), Human Pose and Action Recognition (41 papers) and Domain Adaptation and Few-Shot Learning (22 papers). Chuang Gan is often cited by papers focused on Multimodal Machine Learning Applications (43 papers), Human Pose and Action Recognition (41 papers) and Domain Adaptation and Few-Shot Learning (22 papers). Chuang Gan collaborates with scholars based in United States, China and Hong Kong. Chuang Gan's co-authors include Song Han, Ji Lin, Mingkui Tan, Wenbing Huang, Runhao Zeng, Antonio Torralba, Boqing Gong, Junzhou Huang, Yi Yang and Peihao Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Chuang Gan

109 papers receiving 5.3k citations

Hit Papers

TSM: Temporal Shift Modul... 2019 2026 2021 2023 2019 2019 2023 2024 250 500 750 1000

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Chuang Gan 4.4k 2.6k 711 621 339 114 5.4k
Lorenzo Torresani 4.8k 1.1× 2.2k 0.9× 686 1.0× 408 0.7× 423 1.2× 76 6.1k
Ting Yao 7.4k 1.7× 3.3k 1.3× 729 1.0× 377 0.6× 363 1.1× 220 9.4k
Hong Liu 4.3k 1.0× 1.6k 0.6× 871 1.2× 535 0.9× 488 1.4× 296 6.0k
George Toderici 6.1k 1.4× 2.8k 1.1× 1.1k 1.5× 846 1.4× 599 1.8× 27 7.5k
Caifeng Shan 4.6k 1.1× 988 0.4× 874 1.2× 548 0.9× 618 1.8× 157 5.9k
Ling‐Yu Duan 6.7k 1.5× 1.9k 0.8× 1.2k 1.7× 835 1.3× 524 1.5× 240 7.7k
Marcus Rohrbach 8.4k 1.9× 4.8k 1.9× 774 1.1× 406 0.7× 520 1.5× 42 10.4k
Juan Carlos Niebles 5.0k 1.2× 2.7k 1.0× 825 1.2× 229 0.4× 557 1.6× 66 6.3k
Zhengming Ding 3.5k 0.8× 3.4k 1.3× 385 0.5× 267 0.4× 190 0.6× 143 5.6k
Yuanjun Xiong 5.0k 1.2× 3.1k 1.2× 1.4k 2.0× 264 0.4× 752 2.2× 33 6.6k

Countries citing papers authored by Chuang Gan

Since Specialization
Citations

This map shows the geographic impact of Chuang Gan'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 Chuang Gan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chuang Gan more than expected).

Fields of papers citing papers by Chuang Gan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chuang Gan. 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 Chuang Gan. The network helps show where Chuang Gan may publish in the future.

Co-authorship network of co-authors of Chuang Gan

This figure shows the co-authorship network connecting the top 25 collaborators of Chuang Gan. A scholar is included among the top collaborators of Chuang Gan 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 Chuang Gan. Chuang Gan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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