Dianxi Shi

1.5k total citations
121 papers, 926 citations indexed

About

Dianxi Shi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Dianxi Shi has authored 121 papers receiving a total of 926 indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Computer Vision and Pattern Recognition, 39 papers in Artificial Intelligence and 33 papers in Computer Networks and Communications. Recurrent topics in Dianxi Shi's work include Robotics and Sensor-Based Localization (20 papers), Robotic Path Planning Algorithms (19 papers) and Reinforcement Learning in Robotics (15 papers). Dianxi Shi is often cited by papers focused on Robotics and Sensor-Based Localization (20 papers), Robotic Path Planning Algorithms (19 papers) and Reinforcement Learning in Robotics (15 papers). Dianxi Shi collaborates with scholars based in China, United States and Hong Kong. Dianxi Shi's co-authors include Huaimin Wang, Xinhai Xu, Yuan Li, Gang Yin, Haibo Mi, Ruihao Li, Yangfan Zhou, Yuan Lin, Shaowu Yang and Yongjun Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and IEEE Access.

In The Last Decade

Dianxi Shi

105 papers receiving 896 citations

Peers

Dianxi Shi
Comparison fields: 5 of 82
  • Computer Vision and Pattern Recognition 366
  • Computer Networks and Communications 359
  • Aerospace Engineering 278
  • Artificial Intelligence 226
  • Information Systems 212
Replace Raman Singh with:
Raman Singh India
Asif Khan India
Daibo Liu China
Hyunbum Kim South Korea
Hesham Alhumyani Saudi Arabia
Xukan Ran United States
Soyi Jung South Korea
Eric Schkufza United States
Zhuofan Liao China
Raman Singh India View profile →
Citations per field, relative to Dianxi Shi
Dianxi Shi · 1×
Citations per year, relative to Dianxi Shi
Dianxi Shi · 1×

Countries citing papers authored by Dianxi Shi

Since Specialization
Citations

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

Fields of papers citing papers by Dianxi Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dianxi Shi

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

All Works

20 of 20 papers shown
# Work Indexed citations
1 2
2 0
3 1
4 5
5 1
6 0
7 1
8 8
9 6
10 4
11 16
12 7
13 13
14 13
15 4
16 9
17 21
18 9
19 4
20 215

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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