Naiyu Gao

494 citations
8 papers · 276 indexed · h-index 7
Topics
Advanced Neural Network Applications (4 papers)Domain Adaptation and Few-Shot Learning (4 papers)Advanced Image and Video Retrieval Techniques (3 papers)
Journals
IEEE Transactions on Circuits and Systems for Video TechnologyInternational Journal of Infectious Diseases2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Partner nations
China

In The Last Decade

Naiyu Gao

8 papers receiving 272 citations

Peers

Naiyu Gao
Comparison fields: 5 of 53
  • Computer Vision and Pattern Recognition 237
  • Artificial Intelligence 66
  • Aerospace Engineering 39
  • Media Technology 17
  • Environmental Engineering 16
Replace Simon Reiß with:
Simon Reiß Germany
Kevin J. Shih United States
Mohammadreza Mostajabi Iran
Gianluca Agresti Italy
Buyu Liu United States
Junhyuk Hyun South Korea
Guozhong Luo China
Qi-Zhi Cai United States
Mohsen Zand Malaysia
Yu-Jhe Li United States
Naiyu Gao relative to Simon Reiß Germany Simon Reiß's profile →
Citations per field
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Citations per year

Countries citing papers authored by Naiyu Gao

Since Specialization
Citations

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

Fields of papers citing papers by Naiyu Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Naiyu Gao

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

All Works

8 of 8 papers shown
#WorkIndexed citations
1 20
2 16
3 20
4 22
5 37
6 22
7 138
8 1

About Naiyu Gao

Naiyu Gao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 8 papers that have together received 276 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Advanced Image and Video Retrieval Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (237 citations), Artificial Intelligence (66 citations) and Media Technology (17 citations). Naiyu Gao has collaborated with scholars based in China. Frequent co-authors include Xin Zhao, Kaiqi Huang, Yanhu Shan, Yupei Wang, Yinan Yu, Ming Yang, Jian Jia, Kaiqi Huang, Xiaotang Chen and Tianheng Cheng. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, International Journal of Infectious Diseases 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.

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