Ming Zong

3.4k total citations · 3 hit papers
24 papers, 2.3k citations indexed

About

Ming Zong is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Ming Zong has authored 24 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 4 papers in Computational Mechanics. Recurrent topics in Ming Zong's work include Face and Expression Recognition (10 papers), Human Pose and Action Recognition (8 papers) and Advanced Neural Network Applications (6 papers). Ming Zong is often cited by papers focused on Face and Expression Recognition (10 papers), Human Pose and Action Recognition (8 papers) and Advanced Neural Network Applications (6 papers). Ming Zong collaborates with scholars based in China, New Zealand and United States. Ming Zong's co-authors include Shichao Zhang, Xuelong Li, Xiaofeng Zhu, Ruili Wang, Debo Cheng, Zhenyun Deng, Xiaoshu Zhu, Xuelian Deng, Wanting Ji and Zhe Chen and has published in prestigious journals such as Sensors, Information Sciences and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Ming Zong

24 papers receiving 2.3k citations

Hit Papers

align trajectories

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.

Efficient kNN Classification With Different Numbers of Nearest Neighbors

2017 988 citations Shichao Zhang, Xuelong Li et al. IEEE Transactions on Neural Networks and Learning Systems profile →
Efficient kNN classification algorithm for big data

2016 430 citations Zhenyun Deng, Xiaoshu Zhu et al. Neurocomputing profile →
Learning k for kNN Classification

2017 364 citations Shichao Zhang, Xuelong Li et al. profile →

Peers

Ming Zong

AI

CVPR

IS

BE

EEE

Tareq Abed Mohammed Iraq

Samir Brahim Belhaouari Qatar

Jair Cervantes Mexico

Hafiz Tayyab Rauf United Kingdom

Saad Albawi Iraq

Saad Al-Azawi Iraq

Asdrúbal López‐Chau Mexico

Eyad Elyan United Kingdom

Rung-Ching Chen Taiwan

Fayadh Alenezi Saudi Arabia

Tareq Abed Mohammed Iraq

Ming Zong

754 ×0.8

AI

708 ×1.0

CVPR

175 ×0.9

IS

186 ×1.0

BE

274 ×1.4

EEE

Citations per year, relative to Ming Zong Ming Zong (= 1×) peers Tareq Abed Mohammed

Countries citing papers authored by Ming Zong

Since Specialization

Citations

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

Fields of papers citing papers by Ming Zong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Zong

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Zong. A scholar is included among the top collaborators of Ming Zong 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 Zong. Ming Zong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

He, Long, et al.. (2025). Heparanase inhibition mitigates bleomycin-induced pulmonary fibrosis in mice by reducing M2 macrophage polarization. Immunology Letters. 274. 107006–107006. 4 indexed citations

2.

Zong, Ming, et al.. (2024). Laplacian eigenmaps based manifold regularized CNN for visual recognition. Information Sciences. 689. 121503–121503. 3 indexed citations

3.

Wang, Ruili, et al.. (2023). Convolutional transformer network for fine-grained action recognition. Neurocomputing. 569. 127027–127027. 14 indexed citations

4.

Zhang, Qiangbo, et al.. (2023). Improved YOLOv3 Integrating SENet and Optimized GIoU Loss for Occluded Pedestrian Detection. Sensors. 23(22). 9089–9089. 9 indexed citations

5.

Zong, Ming, et al.. (2023). Deep Learning Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta. Journal of Magnetic Resonance Imaging. 59(2). 510–521. 3 indexed citations

6.

Zong, Ming, et al.. (2022). Spatial and temporal saliency based four-stream network with multi-task learning for action recognition. Applied Soft Computing. 132. 109884–109884. 28 indexed citations

7.

Ji, Wanting, et al.. (2022). Multi-head attention-based two-stream EfficientNet for action recognition. Multimedia Systems. 29(2). 487–498. 28 indexed citations

8.

Zheng, Menghua, Jiayu Xu, Chunwei Tian, et al.. (2022). Attention-based CNNs for Image Classification: A Survey. Journal of Physics Conference Series. 2171(1). 12068–12068. 12 indexed citations

9.

Wang, Lei, et al.. (2021). Multi-cue based four-stream 3D ResNets for video-based action recognition. Information Sciences. 575. 654–665. 22 indexed citations

10.

Zong, Ming, Ruili Wang, Xiu‐Bo Chen, Zhe Chen, & Yuanhao Gong. (2021). Motion saliency based multi-stream multiplier ResNets for action recognition. Image and Vision Computing. 107. 104108–104108. 49 indexed citations

11.

Zheng, Hao, Ruili Wang, Wanting Ji, et al.. (2020). Discriminative deep multi-task learning for facial expression recognition. Information Sciences. 533. 60–71. 69 indexed citations

12.

Zong, Ming, Ruili Wang, Zhe Chen, et al.. (2020). Multi-cue based 3D residual network for action recognition. Neural Computing and Applications. 33(10). 5167–5181. 13 indexed citations

13.

Liu, Mingzhe, et al.. (2020). Toward a robust and fast real-time point cloud registration with factor analysis and Student’s-t mixture model. Journal of Real-Time Image Processing. 17(6). 2005–2014. 1 indexed citations

14.

Liu, Zhenbing, et al.. (2020). Spatiotemporal saliency-based multi-stream networks with attention-aware LSTM for action recognition. Neural Computing and Applications. 32(18). 14593–14602. 38 indexed citations

15.

Chen, Zhe, et al.. (2019). A novel monocular calibration method for underwater vision measurement. Multimedia Tools and Applications. 78(14). 19437–19455. 9 indexed citations

16.

Zhang, Shichao, Xuelong Li, Ming Zong, Xiaofeng Zhu, & Ruili Wang. (2017). Efficient kNN Classification With Different Numbers of Nearest Neighbors. IEEE Transactions on Neural Networks and Learning Systems. 29(5). 1774–1785. 988 indexed citations breakdown →

17.

Wang, Ruili & Ming Zong. (2017). Joint self-representation and subspace learning for unsupervised feature selection. World Wide Web. 21(6). 1745–1758. 8 indexed citations

18.

Deng, Zhenyun, Xiaoshu Zhu, Debo Cheng, Ming Zong, & Shichao Zhang. (2016). Efficient kNN classification algorithm for big data. Neurocomputing. 195. 143–148. 430 indexed citations breakdown →

19.

Deng, Zhenyun, et al.. (2016). Sparse sample self-representation for subspace clustering. Neural Computing and Applications. 29(1). 43–49. 10 indexed citations

20.

Cheng, Debo, Shichao Zhang, Xingyi Liu, Ke Sun, & Ming Zong. (2015). Feature selection by combining subspace learning with sparse representation. Multimedia Systems. 23(3). 285–291. 22 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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