Zihan Ding

1.4k citations
45 papers · 657 · h-index 16

Impact in

    • Multimodal Machine Learning Applications
    • Advanced Neural Network Applications
    • Advanced Image and Video Retrieval Techniques
    • Human Pose and Action Recognition
    • Video Analysis and Summarization
    • Autonomous Vehicle Technology and Safety

Papers in

Zihan Ding

39 papers receiving 643 citations

Peers

Zihan Ding
Comparison fields: 5 of 130
  • Computer Vision and Pattern Recognition 186
  • Automotive Engineering 63
  • Artificial Intelligence 146
  • Control and Systems Engineering 81
  • Earth-Surface Processes 18
Replace Shafriza Nisha Basah with:
Shafriza Nisha Basah Malaysia
Jianjun Li China
Xiaohong Peng China
Leilei Wang China
Seung‐Jae Lee South Korea
You Yang China
Yao Xiao China
Zihan Ding relative to Shafriza Nisha Basah Malaysia Shafriza Nisha Basah's profile →
Citations per field
00.5×4.5×
Shafriza Nisha Basah · 1×
Citations per year

Countries citing papers authored by Zihan Ding

Since Specialization
Citations

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

Fields of papers citing papers by Zihan Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020148
2 202242
3 202041
4 202338
5 202337
6 202135
7 202230
8 202430
9 202428
10 202326
11 202019
12 202119
13 202319
14 202318
15 202416
16 202115
17 201914
18 202114
19 202010
20 20239

About Zihan Ding

Zihan Ding is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Biomedical Engineering and Surgery, having authored 45 papers that have together received 657 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Multimodal Machine Learning Applications (6 papers), Advanced Neural Network Applications (4 papers), Human Pose and Action Recognition (4 papers), Robot Manipulation and Learning (4 papers), Nerve injury and regeneration (3 papers), Nanoplatforms for cancer theranostics (2 papers) and Mesenchymal stem cell research (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (186 citations), Automotive Engineering (63 citations), Artificial Intelligence (146 citations), Control and Systems Engineering (81 citations) and Earth-Surface Processes (18 citations). Zihan Ding has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Hao Dong, Shanghang Zhang, Si Liu, Jizhong Han, Tianrui Hui, Dengbing Yao, Shaofei Huang, Agnes S. Chan, Edward Johns and Tsz‐lok Lee. Their work appears in journals such as ACS Applied Nano Materials, Biomedicines, Chemical Communications, ACS Biomaterials Science & Engineering and Nature Communications.

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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