Faming Shao

830 citations
39 papers · 573 · h-index 14

Impact in

Papers in

Faming Shao

38 papers receiving 544 citations

Peers

Faming Shao
Comparison fields: 5 of 87
  • Computer Vision and Pattern Recognition 312
  • Media Technology 125
  • Civil and Structural Engineering 86
  • Industrial and Manufacturing Engineering 38
  • Automotive Engineering 43
Replace Xuezhi Xiang with:
Xuezhi Xiang China
Ahmed Gomaa Egypt
Chunmian Lin China
Shuang Bai China
Jianan Li China
Gabriele Pieri Italy
Pei An China
Chengyang Li China
Lingyun Bi China
Zhicheng Feng China
Faming Shao relative to Xuezhi Xiang China Xuezhi Xiang's profile →
Citations per field
00.5×1.7×
Xuezhi Xiang · 1×
Citations per year

Countries citing papers authored by Faming Shao

Since Specialization
Citations

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

Fields of papers citing papers by Faming Shao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202061
2 202160
3 202057
4 201952
5 201845
6 201830
7 201920
8 201919
9 202119
10 202118
11 202116
12 202215
13 202114
14 202214
15 202113
16 202212
17 201911
18 202210
19 202210
20 20229

About Faming Shao

Faming Shao is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Aerospace Engineering, Mechanical Engineering and Civil and Structural Engineering, having authored 39 papers that have together received 573 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (21 papers), Advanced Image and Video Retrieval Techniques (10 papers), Video Surveillance and Tracking Methods (10 papers), Infrastructure Maintenance and Monitoring (5 papers), Infrared Target Detection Methodologies (5 papers), Fire Detection and Safety Systems (4 papers), Remote-Sensing Image Classification (4 papers) and Vehicle License Plate Recognition (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (312 citations), Media Technology (125 citations), Civil and Structural Engineering (86 citations), Industrial and Manufacturing Engineering (38 citations) and Automotive Engineering (43 citations). Faming Shao has collaborated with scholars based in China. Frequent co-authors include Xinqing Wang, Fanjie Meng, Dong Wang, Xia Hua, Ting Rui, Guanlin Lu, Yi Xiao, Jingwei Zhu, Jian Tang and Zhijian Yao. Their work appears in journals such as IEEE Access, Defence Technology, Sensors, Electronics and Drones.

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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