Ming Yan

1.9k total citations · 1 hit paper
59 papers, 904 citations indexed

About

Ming Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Ming Yan has authored 59 papers receiving a total of 904 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 26 papers in Artificial Intelligence and 10 papers in Information Systems. Recurrent topics in Ming Yan's work include Multimodal Machine Learning Applications (19 papers), Topic Modeling (12 papers) and Natural Language Processing Techniques (11 papers). Ming Yan is often cited by papers focused on Multimodal Machine Learning Applications (19 papers), Topic Modeling (12 papers) and Natural Language Processing Techniques (11 papers). Ming Yan collaborates with scholars based in China, United States and Singapore. Ming Yan's co-authors include Changsheng Xu, Jitao Sang, Ji Zhang, Rongrong Ji, Yiwei Ma, Xiaoshuai Sun, Fei Huang, Songfang Huang, Bin Bi and Kerry J. Welsh and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Expert Systems with Applications and Sensors.

In The Last Decade

Ming Yan

52 papers receiving 869 citations

Hit Papers

X-CLIP: End-to-End Multi-grained Contrastive Learning for... 2022 2026 2023 2024 2022 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
    2022 141 citations Ming Yan, Ji Zhang et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ming Yan China 16 418 341 125 96 83 59 904
Vijander Singh India 16 120 0.3× 201 0.6× 86 0.7× 21 0.2× 32 0.4× 43 794
Mehdi Hassan Pakistan 18 215 0.5× 201 0.6× 29 0.2× 54 0.6× 18 0.2× 46 722
Chao Che China 16 125 0.3× 296 0.9× 36 0.3× 93 1.0× 8 0.1× 59 793
Zhenzhou Ji China 13 96 0.2× 227 0.7× 58 0.5× 51 0.5× 77 0.9× 74 603
Young-Seob Jeong South Korea 14 70 0.2× 230 0.7× 79 0.6× 57 0.6× 3 0.0× 63 571
Almas Abbasi Pakistan 10 186 0.4× 129 0.4× 164 1.3× 29 0.3× 30 0.4× 29 702
Guangqi Liu China 12 84 0.2× 59 0.2× 26 0.2× 32 0.3× 18 0.2× 53 507
Lin Yao China 20 55 0.1× 310 0.9× 147 1.2× 11 0.1× 13 0.2× 136 1.4k
Zheng Song United States 13 121 0.3× 187 0.5× 95 0.8× 4 0.0× 8 0.1× 71 750
Subrato Bharati Bangladesh 17 73 0.2× 411 1.2× 73 0.6× 17 0.2× 24 0.3× 43 887

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-authorship network of co-authors of Ming Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Yan. A scholar is included among the top collaborators of Ming Yan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming Yan. Ming Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ye, Wei, Haiyang Xu, Qinghao Ye, et al.. (2024). TiMix: Text-Aware Image Mixing for Effective Vision-Language Pre-training. Proceedings of the AAAI Conference on Artificial Intelligence. 38(3). 2489–2497.
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Ye, Jiabo, Ming Yan, Haiyang Xu, et al.. (2024). UniQRNet: Unifying Referring Expression Grounding and Segmentation with QRNet. ACM Transactions on Multimedia Computing Communications and Applications. 20(8). 1–28. 3 indexed citations
5.
Liu, Haowei, Feng Zhang, Haiyang Xu, et al.. (2024). MIBench: Evaluating Multimodal Large Language Models over Multiple Images. 22417–22428.
6.
Jiang, Han, Ming Yan, Ji Zhang, et al.. (2024). Revisiting Unsupervised Temporal Action Localization: The Primacy of High-Quality Actionness and Pseudolabels. 5643–5652. 2 indexed citations
7.
Tang, Long, Ping Xu, Yian Liu, et al.. (2023). A novel self-attention model based on cosine self-similarity for cancer classification of protein mass spectrometry. International Journal of Mass Spectrometry. 494. 117131–117131. 4 indexed citations
8.
Xu, Ping, et al.. (2023). Fentanyl analogs classification via Siamese network and mass spectral library searching. Expert Systems with Applications. 217. 119534–119534. 1 indexed citations
9.
Xu, Haiyang, Wei Ye, Qinghao Ye, et al.. (2023). COPA : Efficient Vision-Language Pre-training through Collaborative Object- and Patch-Text Alignment. 4480–4491. 7 indexed citations
10.
Yan, Ming, et al.. (2023). Synthesis of sulfinamides via photocatalytic alkylation or arylation of sulfinylamine. Organic & Biomolecular Chemistry. 22(2). 348–352. 17 indexed citations
11.
Zhang, Haoyu, Meng Liu, Yuhong Li, et al.. (2023). Attribute-Guided Collaborative Learning for Partial Person Re-Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 14144–14160. 17 indexed citations
12.
Yan, Ming, et al.. (2023). From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping. 4326–4334. 2 indexed citations
13.
Xu, Yang, Haiyang Xu, Hanwang Zhang, et al.. (2023). Learning Trajectory-Word Alignments for Video-Language Tasks. 33. 2504–2514. 1 indexed citations
14.
Wang, Xu, Dezhong Peng, Ming Yan, & Peng Hu. (2023). Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 10200–10208. 13 indexed citations
15.
Li, Chenliang, Ming Yan, Weizhou Shen, et al.. (2023). ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models. 566–578. 5 indexed citations
16.
Wang, Xuwu, Junfeng Tian, Min Gui, et al.. (2022). WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 4785–4797. 18 indexed citations
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Xu, Haiyang, Ming Yan, Chenliang Li, et al.. (2021). E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning. 503–513. 52 indexed citations
19.
Su, John R., Michael M. McNeil, Kerry J. Welsh, et al.. (2021). Myopericarditis after vaccination, Vaccine Adverse Event Reporting System (VAERS), 1990–2018. Vaccine. 39(5). 839–845. 113 indexed citations
20.
Yan, Ming, Jitao Sang, & Changsheng Xu. (2014). Mining Cross-network Association for YouTube Video Promotion. 557–566. 30 indexed citations

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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