Mingyang Ling

433 citations
12 papers · 251 · h-index 5

Impact in

    • Multimodal Machine Learning Applications
    • Advanced Image and Video Retrieval Techniques
    • Human Pose and Action Recognition
    • Advanced Neural Network Applications
    • Image Enhancement Techniques
    • Video Analysis and Summarization
    • Domain Adaptation and Few-Shot Learning
    • Topic Modeling

Papers in

Mingyang Ling

8 papers receiving 247 citations

Peers

Mingyang Ling
Comparison fields: 5 of 37
  • Computer Vision and Pattern Recognition 220
  • Artificial Intelligence 132
  • Media Technology 10
  • Computer Graphics and Computer-Aided Design 3
  • Geography, Planning and Development 3
Replace Chengjian Feng with:
Chengjian Feng China
Arthur Douillard France
Alexandre Ramé France
Florian Bordes Canada
Yuyu Guo China
WonJun Moon South Korea
Huaijia Lin Hong Kong
Max Vladymyrov United States
Yuanhan Zhang Singapore
Hongtao Xie China
Mingyang Ling relative to Chengjian Feng China Chengjian Feng's profile →
Citations per field
00.5×10×15×21.5×
Chengjian Feng · 1×
Citations per year

Countries citing papers authored by Mingyang Ling

Since Specialization
Citations

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

Fields of papers citing papers by Mingyang Ling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Mingyang Ling, 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 Mingyang Ling Line = papers co-authored together Mingyang Ling links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2019213
2 202412
3 20168
4 20248
5 20254
6 20243
7 20242
8 20241
9 20240
10 20230
11 20260
12 20250

About Mingyang Ling

Mingyang Ling is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Information Systems and Infectious Diseases, having authored 12 papers that have together received 251 indexed citations. Recurring topics across this work include Image Enhancement Techniques (9 papers), Advanced Image Processing Techniques (5 papers), Advanced Vision and Imaging (4 papers), Advanced Image Fusion Techniques (3 papers), Image and Signal Denoising Methods (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (1 paper) and Web Data Mining and Analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (220 citations), Artificial Intelligence (132 citations), Media Technology (10 citations), Computer Graphics and Computer-Aided Design (3 citations) and Geography, Planning and Development (3 citations). Mingyang Ling has collaborated with scholars based in China, United States and Netherlands. Frequent co-authors include Jiuxiang Gu, Handong Zhao, Sheng Li, Jianfei Cai, Zhe Lin, Kan Chang, Richard C. Wang, Shuping Dang, Lidong Bing and William W. Cohen. Their work appears in journals such as Knowledge-Based Systems, IEEE Transactions on Computational Imaging, Computers & Graphics, Neural Networks and Lecture notes in computer science.

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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