Ming Yan

1.9k citations
59 papers · 904 indexed · 1 hit paper · h-index 16

Impact in

Papers in

Ming Yan

52 papers receiving 869 citations

Hit Papers

X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval 2022 · 141 citations
1410+1+2Years since publication4080120

Peers

Ming Yan
Comparison fields: 5 of 116
  • Computer Vision and Pattern Recognition 418
  • Artificial Intelligence 341
  • Information Systems 125
  • Infectious Diseases 83
  • Cardiology and Cardiovascular Medicine 96
Replace Zhenzhou Ji with:
Zhenzhou Ji China
Mehdi Hassan Pakistan
Vijander Singh India
Chao Che China
Zhongliang Yang China
Seyed Vahid Moravvej Iran
Guangqi Liu China
Wenchao Yu United States
Zheng Song United States
Lianhua Chi Australia
Ming Yan relative to Zhenzhou Ji China Zhenzhou Ji's profile →
Citations per field
00.5×
Zhenzhou Ji · 1×
Citations per year

Countries citing papers authored by Ming Yan

Since Specialization
Citations

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

Fields of papers citing papers by Ming Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
Hit paper breakdown →
2022141
2 2021113
3 201565
4 202156
5 202152
6 202246
7 201540
8 201430
9 202026
10 201624
11 202023
12 202322
13 202319
14 202218
15 202317
16 202317
17 202313
18 202213
19 202213
20 202412

About Ming Yan

Ming Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Surgery and Computer Networks and Communications, having authored 59 papers that have together received 904 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (19 papers), Topic Modeling (12 papers), Natural Language Processing Techniques (11 papers), Domain Adaptation and Few-Shot Learning (7 papers), Human Pose and Action Recognition (7 papers), Advanced Image and Video Retrieval Techniques (5 papers), Recommender Systems and Techniques (4 papers) and Caching and Content Delivery (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (418 citations), Artificial Intelligence (341 citations), Information Systems (125 citations), Infectious Diseases (83 citations) and Cardiology and Cardiovascular Medicine (96 citations). Ming Yan has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Changsheng Xu, Jitao Sang, Ji Zhang, Xiaoshuai Sun, Yiwei Ma, Rongrong Ji, Fei Huang, Songfang Huang, Bin Bi and Kerry J. Welsh. Their work appears in journals such as ACM Transactions on Multimedia Computing Communications and Applications, Ceramics International, Medicine, IEEE Transactions on Multimedia and Expert Systems with Applications.

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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