Daming Shi

1.7k citations
88 papers · 1.3k · h-index 22

Impact in

Papers in

Daming Shi

81 papers receiving 1.3k citations

Peers

Daming Shi
Comparison fields: 5 of 108
  • Media Technology 232
  • Computer Vision and Pattern Recognition 490
  • Catalysis 82
  • Signal Processing 114
  • Artificial Intelligence 291
Replace Yinghua Lu with:
Yinghua Lu China
Xiaodong Zhang China
Jing Dong China
Weiwei Wang China
Chunkai Zhang China
Stephen D. Brown Canada
Guangrun Wang China
Stefanie Jegelka United States
Xinghua Qu China
Daming Shi relative to Yinghua Lu China Yinghua Lu's profile →
Citations per field
00.5×2×4×6×7.5×
Yinghua Lu · 1×
Citations per year

Countries citing papers authored by Daming Shi

Since Specialization
Citations

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

Fields of papers citing papers by Daming Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daming Shi, 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 Daming Shi Line = papers co-authored together Daming Shi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 88 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019111
2 2002103
3 201594
4 202181
5 201269
6 201059
7 201657
8 202255
9 202040
10 201040
11 202038
12 200937
13 200334
14 200933
15 202232
16 201230
17 202227
18 199826
19 202125
20 200425

About Daming Shi

Daming Shi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Signal Processing and Biomedical Engineering, having authored 88 papers that have together received 1.3k indexed citations. Recurring topics across this work include Neural Networks and Applications (14 papers), Advanced Image Processing Techniques (11 papers), Image and Signal Denoising Methods (10 papers), Domain Adaptation and Few-Shot Learning (8 papers), Image Enhancement Techniques (8 papers), Catalysis for Biomass Conversion (7 papers), Machine Learning and ELM (7 papers) and Robotics and Sensor-Based Localization (7 papers). The work is most often cited by research in Media Technology (232 citations), Computer Vision and Pattern Recognition (490 citations), Catalysis (82 citations), Signal Processing (114 citations) and Artificial Intelligence (291 citations). Daming Shi has collaborated with scholars based in China, Singapore and United Kingdom. Frequent co-authors include John M. Vohs, Bin Deng, Liying Zheng, Sen Jia, Wing W. Y. Ng, Xiaochun Cheng, Chuanjun Zheng, Eric C.C. Tsang, Maysam Orouskhani and Jingtao Zhang. Their work appears in journals such as IEEE Access, Pattern Recognition Letters, Computer Vision and Image Understanding, Remote Sensing and International Journal of Neural Systems.

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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