Peijin Wang

3.3k citations
102 papers · 2.4k · 2 hit papers · h-index 24

Impact in

Papers in

Peijin Wang

93 papers receiving 2.4k citations

Peijin Wang's Hit Papers

FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery 2022 · 353 citations
3530+1+2Years since publication100200300

Peers

Peijin Wang
Comparison fields: 5 of 137
  • Media Technology 759
  • Computer Vision and Pattern Recognition 1.1k
  • Aerospace Engineering 376
  • Electrochemistry 84
  • Artificial Intelligence 341
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Citations per field
00.5×1.5×1.9×
Shilin Zhou · 1×
Citations per year

Countries citing papers authored by Peijin Wang

Since Specialization
Citations

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

Fields of papers citing papers by Peijin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Peijin Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Peijin Wang Line = papers co-authored together Peijin Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 102 papers — load more, or switch the sort, to bring in the rest.

#Work
1
FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery
Hit paper breakdown →
2022353
2 2019245
3 2020220
4
RingMo: A Remote Sensing Foundation Model With Masked Image Modeling
Hit paper breakdown →
2022194
5 2021153
6 202367
7 202366
8 202165
9 202061
10 201957
11 202057
12 201849
13 201847
14 202141
15 202339
16 201938
17 202036
18 202335
19 201835
20 201431

About Peijin Wang

Peijin Wang is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Artificial Intelligence, Control and Systems Engineering and Molecular Biology, having authored 102 papers that have together received 2.4k indexed citations. Recurring topics across this work include Remote-Sensing Image Classification (20 papers), Advanced Image and Video Retrieval Techniques (18 papers), Domain Adaptation and Few-Shot Learning (11 papers), Advanced Neural Network Applications (10 papers), Industrial Technology and Control Systems (9 papers), Advanced biosensing and bioanalysis techniques (8 papers), Advanced Sensor and Control Systems (7 papers) and Advanced Computational Techniques and Applications (6 papers). The work is most often cited by research in Media Technology (759 citations), Computer Vision and Pattern Recognition (1.1k citations), Aerospace Engineering (376 citations), Electrochemistry (84 citations) and Artificial Intelligence (341 citations). Peijin Wang has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Xian Sun, Kun Fu, Wenhui Diao, Cheng Wang, Hongwei Zhang, Ruiping Wang, Yingchao Feng, Xiaonan Lü, Zhiyuan Yan and Chunfeng Yuan. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, Journal of Electroanalytical Chemistry, IEEE Transactions on Pattern Analysis and Machine Intelligence and Environmental Toxicology.

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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