Qing Ping

478 citations
16 papers · 294 · h-index 11

Impact in

Papers in

    • Video Analysis and Summarization 4
    • Data Visualization and Analytics 3
    • Multimodal Machine Learning Applications 3
    • Human Pose and Action Recognition 2
    • Advanced Image and Video Retrieval Techniques 2
    • Domain Adaptation and Few-Shot Learning 2
    • Advanced Text Analysis Techniques 2
    • Topic Modeling 2

Qing Ping

16 papers receiving 286 citations

Peers

Qing Ping
Comparison fields: 5 of 101
  • Computer Vision and Pattern Recognition 107
  • Artificial Intelligence 94
  • Health Informatics 2
  • Library and Information Sciences 2
  • Computer Graphics and Computer-Aided Design 4
Replace Daniel Seebacher with:
Daniel Seebacher Germany
Arpad Rimmel France
Jae Youn Ahn South Korea
Gongbo Tang Sweden
Jingqiang Chen China
David Boaz Israel
Kai-Hui Lee Taiwan
Norhalina Senan Malaysia
Gourab Kundu United States
Qing Ping relative to Daniel Seebacher Germany Daniel Seebacher's profile →
Citations per field
00.5×1.5×
Daniel Seebacher · 1×
Citations per year

Countries citing papers authored by Qing Ping

Since Specialization
Citations

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

Fields of papers citing papers by Qing Ping

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

16 of 16 papers shown
#Work
1 201768
2 202249
3 201532
4 201722
5 201920
6 201919
7 201616
8 202313
9 201812
10 201712
11 202111
12 20187
13 20187
14 20164
15 20151
16 20221

About Qing Ping

Qing Ping is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Sociology and Political Science, Molecular Biology and Health, having authored 16 papers that have together received 294 indexed citations. Recurring topics across this work include Video Analysis and Summarization (4 papers), Data Visualization and Analytics (3 papers), Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Text Analysis Techniques (2 papers), Topic Modeling (2 papers), Human Pose and Action Recognition (2 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (107 citations), Artificial Intelligence (94 citations), Health Informatics (2 citations), Library and Information Sciences (2 citations) and Computer Graphics and Computer-Aided Design (4 citations). Qing Ping has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Chaomei Chen, Jiangen He, Prem Natarajan, Christopher C. Yang, Edward H. Ip, Nancy E. Avis, Feng Gao, Ying Wu, Xiwei Tang and Xiaohua Hu. Their work appears in journals such as Journal of the Association for Information Science and Technology, BMC Bioinformatics, Scientometrics, Quality of Life Research and World Wide Web.

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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