Jinyu Yang

2.2k citations
44 papers · 1.2k · 1 hit paper · h-index 19

Impact in

Papers in

Jinyu Yang

41 papers receiving 1.2k citations

Jinyu Yang's Hit Papers

Vision-Language Pre-Training with Triple Contrastive Learning 2022 · 179 citations
1790+1+2Years since publication50100150

Peers

Jinyu Yang
Comparison fields: 5 of 135
  • Computer Vision and Pattern Recognition 276
  • Artificial Intelligence 325
  • Clinical Biochemistry 61
  • Health Informatics 10
  • Molecular Biology 476
Replace Chenxin Li with:
Chenxin Li China
Xiaoli Wang China
Jeesoo Kim South Korea
Xinyue Liu China
Jónathan Heras Spain
Alfonso Rodríguez‐Patón Spain
Yijia Liu China
Lonnie R. Welch United States
Katherine M. Collins United States
S. Karthikeyan United States
Jinyu Yang relative to Chenxin Li China Chenxin Li's profile →
Citations per field
00.5×7.6×
Chenxin Li · 1×
Citations per year

Countries citing papers authored by Jinyu Yang

Since Specialization
Citations

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

Fields of papers citing papers by Jinyu Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017254
2
Vision-Language Pre-Training with Triple Contrastive Learning
Hit paper breakdown →
2022179
3 202391
4 201950
5 201944
6 201744
7 201944
8 202144
9 202143
10 201642
11 201739
12 202238
13 201730
14 202127
15 202123
16 201822
17 202021
18 201720
19 202218
20 202416

About Jinyu Yang

Jinyu Yang is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Food Science and Plant Science, having authored 44 papers that have together received 1.2k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (5 papers), Domain Adaptation and Few-Shot Learning (5 papers), Genomics and Phylogenetic Studies (5 papers), RNA and protein synthesis mechanisms (4 papers), Genomics and Chromatin Dynamics (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Polysaccharides and Plant Cell Walls (3 papers) and Polysaccharides Composition and Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (276 citations), Artificial Intelligence (325 citations), Clinical Biochemistry (61 citations), Health Informatics (10 citations) and Molecular Biology (476 citations). Jinyu Yang has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Junzhou Huang, Qin Ma, Song Gao, Belinda Zeng, Jiali Duan, Yi Xu, Trishul Chilimbi, Son N. Tran, Li‐Qun Chen and Adam McDermaid. Their work appears in journals such as Bioinformatics, Briefings in Bioinformatics, Food Chemistry, Journal of Computational Biology and Carbohydrate Polymers.

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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