Ming Tang

5.7k total citations · 1 hit paper
110 papers, 2.3k citations indexed

About

Ming Tang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Ming Tang has authored 110 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Computer Vision and Pattern Recognition, 24 papers in Artificial Intelligence and 13 papers in Aerospace Engineering. Recurrent topics in Ming Tang's work include Video Surveillance and Tracking Methods (40 papers), Advanced Neural Network Applications (22 papers) and Human Pose and Action Recognition (18 papers). Ming Tang is often cited by papers focused on Video Surveillance and Tracking Methods (40 papers), Advanced Neural Network Applications (22 papers) and Human Pose and Action Recognition (18 papers). Ming Tang collaborates with scholars based in China, United States and Hong Kong. Ming Tang's co-authors include Jinqiao Wang, Yingying Chen, Hanqing Lu, Jiayi Feng, Yancheng Bai, Bingke Zhu, Zhengtao Yu, Weijiang Li, Fang Qi and Chaoyang Zhao and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Cleaner Production and IEEE Transactions on Image Processing.

In The Last Decade

Ming Tang

101 papers receiving 2.3k citations

Hit Papers

AnomalyGPT: Detecting Ind... 2024 2026 2024 20 40 60
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. AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
    2024 73 citations Bingke Zhu, Guibo Zhu 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 Tang China 25 1.7k 558 263 216 154 110 2.3k
Longyin Wen China 27 2.4k 1.4× 554 1.0× 508 1.9× 192 0.9× 163 1.1× 49 2.8k
Dawei Du United States 22 1.8k 1.1× 529 0.9× 516 2.0× 200 0.9× 134 0.9× 41 2.3k
Bo Wu United States 21 2.6k 1.5× 876 1.6× 255 1.0× 165 0.8× 202 1.3× 62 2.9k
Zhaowei Cai United States 9 1.6k 1.0× 588 1.1× 260 1.0× 207 1.0× 161 1.0× 15 2.2k
Jiangmiao Pang China 15 2.5k 1.5× 847 1.5× 691 2.6× 305 1.4× 146 0.9× 31 3.2k
Jan Hosang Germany 9 2.0k 1.2× 562 1.0× 324 1.2× 191 0.9× 87 0.6× 10 2.4k
Kang-Hyun Jo South Korea 26 1.9k 1.1× 363 0.7× 333 1.3× 475 2.2× 234 1.5× 353 2.7k
Xizhou Zhu China 15 1.9k 1.1× 644 1.2× 321 1.2× 249 1.2× 141 0.9× 25 2.7k

Countries citing papers authored by Ming Tang

Since Specialization
Citations

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

Fields of papers citing papers by Ming Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Tang. A scholar is included among the top collaborators of Ming Tang 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 Tang. Ming Tang 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.
Wu, Jie, et al.. (2025). Moving object detection based on ViBe long-term background modeling. Digital Signal Processing. 158. 104976–104976.
2.
Tang, Ming, et al.. (2024). Research on the Effect of Structural Parameters on Cavitation Performance of Shear Hydrodynamic Cavitation Generator. Applied Sciences. 14(9). 3676–3676. 3 indexed citations
3.
Dai, Yun, et al.. (2024). Metal ion formulations for diabetic wound healing: Mechanisms and therapeutic potential. International Journal of Pharmaceutics. 667. 124889–124889. 5 indexed citations
4.
Hu, Qinwen, et al.. (2024). General Speech Restoration Using Two-Stage Generative Adversarial Networks. 31–32. 1 indexed citations
5.
Li, Zhaowen, Yousong Zhu, Wei Li, et al.. (2024). Efficient Masked Autoencoders With Self-Consistency. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 8743–8757. 1 indexed citations
6.
7.
8.
Zhu, Bingke, et al.. (2024). FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization. 2041–2049. 11 indexed citations
9.
Zhao, Xu, et al.. (2023). Pruning-aware Sparse Regularization for Network Pruning. 20(1). 109–120. 9 indexed citations
10.
Zhu, Kuan, Haiyun Guo, Shiliang Zhang, et al.. (2023). AAformer: Auto-Aligned Transformer for Person Re-Identification. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 17307–17317. 81 indexed citations
11.
Chen, Kaimin, et al.. (2023). An efficient and eco-friendly operation mode for container transshipments through optimizing the inter-terminal truck routing problem. Journal of Cleaner Production. 430. 139644–139644. 2 indexed citations
12.
Zhu, Kuan, et al.. (2022). Learning Semantics-Consistent Stripes With Self-Refinement for Person Re-Identification. IEEE Transactions on Neural Networks and Learning Systems. 34(11). 8531–8542. 14 indexed citations
13.
Chen, Yingying, et al.. (2022). Human Parsing With Part-Aware Relation Modeling. IEEE Transactions on Multimedia. 25. 2601–2612. 10 indexed citations
14.
Zhu, Kuan, Haiyun Guo, Qinghai Miao, et al.. (2022). Multi-Granularity Mutual Learning Network for Object Re-Identification. IEEE Transactions on Intelligent Transportation Systems. 23(9). 15178–15189. 18 indexed citations
15.
Chen, Yingying, et al.. (2022). Grammar-Induced Wavelet Network for Human Parsing. IEEE Transactions on Image Processing. 31. 4502–4514. 7 indexed citations
16.
Guo, Haiyun, Jianguo Hu, Xu Zhao, et al.. (2020). A novel data augmentation scheme for pedestrian detection with attribute preserving GAN. Neurocomputing. 401. 123–132. 32 indexed citations
17.
Li, Weijiang, Fang Qi, Ming Tang, & Zhengtao Yu. (2020). Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification. Neurocomputing. 387. 63–77. 216 indexed citations
18.
Zheng, Linyu, et al.. (2019). Learning Features with Differentiable Closed-Form Solver for Tracking. arXiv (Cornell University). 1 indexed citations
19.
Gai, Shaoyan, Feipeng Da, & Ming Tang. (2019). A flexible multi-view calibration and 3D measurement method based on digital fringe projection. Measurement Science and Technology. 30(2). 25203–25203. 22 indexed citations
20.
Zhao, Xu, Chaoyang Zhao, Haiyun Guo, et al.. (2018). Elite Loss for scene text detection. Neurocomputing. 333. 284–291. 4 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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