Ming Shao

4.2k total citations
115 papers, 2.7k citations indexed

About

Ming Shao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Ming Shao has authored 115 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Computer Vision and Pattern Recognition, 43 papers in Artificial Intelligence and 16 papers in Computational Mechanics. Recurrent topics in Ming Shao's work include Face recognition and analysis (26 papers), Face and Expression Recognition (26 papers) and Domain Adaptation and Few-Shot Learning (19 papers). Ming Shao is often cited by papers focused on Face recognition and analysis (26 papers), Face and Expression Recognition (26 papers) and Domain Adaptation and Few-Shot Learning (19 papers). Ming Shao collaborates with scholars based in United States, China and Mexico. Ming Shao's co-authors include Yun Fu, Zhengming Ding, Siyu Xia, Sheng Li, Dmitry Kit, Hongfu Liu, Yun Fu, Jiebo Luo, Joseph P. Robinson and Changsheng Lu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Nanoscale.

In The Last Decade

Ming Shao

99 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ming Shao United States	32	1.8k	981	483	241	229	115	2.7k
Gregory Shakhnarovich United States	26	2.3k 1.3×	819 0.8×	264 0.5×	252 1.0×	376 1.6×	45	3.1k
Yun Fu United States	27	3.2k 1.8×	551 0.6×	727 1.5×	102 0.4×	216 0.9×	89	3.7k
Liming Chen France	29	1.6k 0.9×	352 0.4×	519 1.1×	153 0.6×	193 0.8×	91	2.7k
Wen-Huang Cheng Taiwan	28	1.8k 1.0×	559 0.6×	119 0.2×	219 0.9×	185 0.8×	116	3.0k
Jia Li China	35	3.1k 1.7×	780 0.8×	269 0.6×	319 1.3×	109 0.5×	172	4.1k
Xiaoshuai Sun China	34	3.2k 1.8×	1.2k 1.3×	135 0.3×	169 0.7×	135 0.6×	163	3.7k
Walter J. Scheirer United States	26	1.9k 1.0×	1.7k 1.8×	791 1.6×	121 0.5×	199 0.9×	104	3.8k
Yaniv Taigman Israel	10	4.0k 2.2×	929 0.9×	1.4k 3.0×	174 0.7×	194 0.8×	13	4.9k
Leon A. Gatys Germany	11	3.1k 1.8×	676 0.7×	183 0.4×	539 2.2×	84 0.4×	16	4.0k
Tomas Pfister United States	17	1.7k 1.0×	1.3k 1.3×	231 0.5×	151 0.6×	169 0.7×	52	3.0k

Countries citing papers authored by Ming Shao

Since Specialization

Citations

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

Fields of papers citing papers by Ming Shao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Shao. A scholar is included among the top collaborators of Ming Shao 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 Shao. Ming Shao 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

Shao, Ming, et al.. (2026). Robust defense strategies for multimodal contrastive learning: efficient fine-tuning against backdoor attacks. Multimedia Tools and Applications. 85(1).

Liu, Zhendong, et al.. (2024). Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition. 1–10.

McLinden, John, et al.. (2024). Topology-Aware Multimodal Fusion for Neural Dynamics Representation Learning and Classification. IEEE Sensors Journal. 24(13). 21062–21073. 2 indexed citations

Shao, Ming, et al.. (2024). Utilizing Inherent Bias for Memory Efficient Continual Learning: A Simple and Robust Baseline. Image and Vision Computing. 151. 105288–105288.

Shao, Ming, et al.. (2023). High‐precision skeleton‐based human repetitive action counting. IET Computer Vision. 17(6). 700–709. 3 indexed citations

Zhang, Lihao, Yufei Chen, Yue Cao, et al.. (2023). Bioinspired hierarchical colloidal crystal paper with Janus wettability for oil/water separation and heavy metal ion removal. Nanoscale. 15(29). 12212–12219. 6 indexed citations

Xiao, Gao, et al.. (2023). The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review. Biosensors. 13(3). 389–389. 16 indexed citations

Shao, Ming, et al.. (2022). Generative Adversarial Attack on Ensemble Clustering. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3839–3848.

Robinson, Joseph P., Ming Shao, & Yun Fu. (2020). Visual Kinship Recognition: A Decade in the Making. arXiv (Cornell University). 2 indexed citations

10.

Lu, Changsheng, Siyu Xia, Ming Shao, & Yun Fu. (2019). Arc-Support Line Segments Revisited: An Efficient High-Quality Ellipse Detection. IEEE Transactions on Image Processing. 29. 768–781. 107 indexed citations

11.

Ding, Zhengming & Ming Shao. (2019). Robust Knowledge Discovery via Low-rank Modeling.. arXiv (Cornell University). 1 indexed citations

12.

Shao, Ming, et al.. (2018). Deep Evolutionary 3D Diffusion Heat Maps for Large-pose Face Alignment.. British Machine Vision Conference. 256. 4 indexed citations

13.

Lu, Changsheng, Siyu Xia, Ming Shao, & Yun Fu. (2018). High-quality Ellipse Detection Based on Arc-support Line Segments.. arXiv (Cornell University). 3 indexed citations

14.

Ding, Zhengming, Ming Shao, Sheng Li, & Yun Fu. (2018). Generic Embedded Semantic Dictionary for Robust Multi-Label Classification. 7. 282–289. 2 indexed citations

15.

Jia, Chengcheng, Ming Shao, Sheng Li, Handong Zhao, & Yun Fu. (2017). Stacked Denoising Tensor Auto-Encoder for Action Recognition With Spatiotemporal Corruptions. IEEE Transactions on Image Processing. 27(4). 1878–1887. 19 indexed citations

16.

Shao, Ming, Sheng Li, Zhengming Ding, & Yun Fu. (2015). Deep linear coding for fast graph clustering. International Conference on Artificial Intelligence. 3798–3804. 26 indexed citations

17.

Li, Sheng, Ming Shao, & Yun Fu. (2015). Cross-view projective dictionary learning for person re-identification. International Conference on Artificial Intelligence. 2155–2161. 79 indexed citations

18.

Ding, Zhengming, Ming Shao, & Yun Fu. (2015). Deep low-rank coding for transfer learning. International Conference on Artificial Intelligence. 3453–3459. 56 indexed citations

19.

Liu, Mengmeng, et al.. (2015). The Research on Impact Factors of Perceived Online Review Usefulness. International Conference on Management Science and Engineering. 9(1). 36–44. 2 indexed citations

20.

Shao, Ming & Yunhong Wang. (2009). Extracting intrinsic images from multi-spectral. 241–246. 2 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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