Shaoxin Li

1.2k total citations · 1 hit paper
19 papers, 687 citations indexed

About

Shaoxin Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Shaoxin Li has authored 19 papers receiving a total of 687 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 4 papers in Signal Processing. Recurrent topics in Shaoxin Li's work include Face recognition and analysis (8 papers), Face and Expression Recognition (7 papers) and Biometric Identification and Security (4 papers). Shaoxin Li is often cited by papers focused on Face recognition and analysis (8 papers), Face and Expression Recognition (7 papers) and Biometric Identification and Security (4 papers). Shaoxin Li collaborates with scholars based in China, Poland and Singapore. Shaoxin Li's co-authors include Yuge Huang, Pengcheng Shen, Jilin Li, Feiyue Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Shiguang Shan, Xilin Chen and Junqing Le and has published in prestigious journals such as Accounts of Chemical Research, IEEE Transactions on Image Processing and Neurocomputing.

In The Last Decade

Shaoxin Li

16 papers receiving 664 citations

Hit Papers

CurricularFace: Adaptive Curriculum Learning Loss for Dee... 2020 2026 2022 2024 2020 100 200 300
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. CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
    2020 340 citations Yuge Huang, Yuhan Wang 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
Shaoxin Li China 9 554 234 164 34 23 19 687
Grigory Antipov France 4 382 0.7× 86 0.4× 105 0.6× 13 0.4× 12 0.5× 5 502
Samarth Bharadwaj India 15 784 1.4× 708 3.0× 100 0.6× 158 4.6× 15 0.7× 27 979
Moez Baccouche France 5 377 0.7× 68 0.3× 115 0.7× 9 0.3× 12 0.5× 6 486
Xunqiang Tao China 10 518 0.9× 245 1.0× 212 1.3× 81 2.4× 3 0.1× 14 673
Tejas I. Dhamecha India 12 469 0.8× 378 1.6× 131 0.8× 93 2.7× 1 0.0× 22 645
Yaoyao Zhong China 10 341 0.6× 143 0.6× 144 0.9× 10 0.3× 5 0.2× 23 453
Heydi Méndez-Vázquez Mexico 13 397 0.7× 221 0.9× 35 0.2× 37 1.1× 2 0.1× 34 463
Xingbo Dong China 12 250 0.5× 143 0.6× 76 0.5× 60 1.8× 2 0.1× 40 349
Elham Tabassi United States 9 464 0.8× 446 1.9× 84 0.5× 109 3.2× 4 0.2× 27 677
Iwan Setyawan Indonesia 11 792 1.4× 118 0.5× 76 0.5× 56 1.6× 2 0.1× 82 914

Countries citing papers authored by Shaoxin Li

Since Specialization
Citations

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

Fields of papers citing papers by Shaoxin Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaoxin Li

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

All Works

19 of 19 papers shown
1.
Li, Shaoxin, et al.. (2026). Chemical Reactions at Electrified Interfaces. Accounts of Chemical Research. 59(2). 285–297.
2.
Fang, Hui, et al.. (2025). DUGCN: Deep Unfolding Gradient Consistency Network for fast MRI reconstruction. Neurocomputing. 646. 130481–130481.
3.
Bi, Zhichun, et al.. (2024). AFM Super-Resolution Reconstruction Neural Network for Imaging Nanomaterials. ACS Applied Nano Materials. 7(22). 25470–25479. 2 indexed citations
4.
Huang, Yuge, Pengcheng Shen, Shaoxin Li, et al.. (2021). Consistent Instance False Positive Improves Fairness in Face Recognition. 578–586. 29 indexed citations
5.
Shen, Li, et al.. (2021). Spherical Confidence Learning for Face Recognition. 15624–15632. 36 indexed citations
6.
Li, Jie, Rongrong Ji, Peixian Chen, et al.. (2021). Aha! Adaptive History-driven Attack for Decision-based Black-box Models. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 16148–16157. 8 indexed citations
7.
Zhang, Tong, Zhen Cui, Yuge Huang, et al.. (2021). Graph Game Embedding. Proceedings of the AAAI Conference on Artificial Intelligence. 35(9). 7711–7720. 1 indexed citations
8.
Cui, Zhen, Chunyan Xu, Yuge Huang, et al.. (2021). Scribble-Supervised Semantic Segmentation Inference. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 15334–15343. 3 indexed citations
9.
Zhang, Tong, Xueya Zhang, Zhen Cui, et al.. (2021). Wasserstein Coupled Graph Learning for Cross-Modal Retrieval. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 1793–1802. 16 indexed citations
10.
Zhang, Ruixin, Yuge Huang, Shaoxin Li, et al.. (2021). SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance. 7666–7675. 66 indexed citations
11.
Huang, Yuge, Pengcheng Shen, Ying Tai, et al.. (2020). Distribution Distillation Loss: Generic Approach for Improving Face Recognition from Hard Samples. arXiv (Cornell University). 1 indexed citations
12.
Huang, Yuge, Yuhan Wang, Ying Tai, et al.. (2020). CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition. 5900–5909. 340 indexed citations breakdown →
13.
Li, Shaoxin, Nankun Mu, Junqing Le, & Xiaofeng Liao. (2019). A novel algorithm for privacy preserving utility mining based on integer linear programming. Engineering Applications of Artificial Intelligence. 81. 300–312. 15 indexed citations
14.
Li, Shaoxin, Nankun Mu, Junqing Le, & Xiaofeng Liao. (2019). Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction. Computers & Security. 84. 17–34. 27 indexed citations
15.
Li, Shaoxin, Nankun Mu, & Xiaofeng Liao. (2018). Privacy Preserving Frequent Itemsets Mining Based on Database Reconstruction. 388–394.
16.
Li, Shaoxin, Shiguang Shan, Shuicheng Yan, & Xilin Chen. (2016). Relative Forest for Visual Attribute Prediction. IEEE Transactions on Image Processing. 25(9). 3991–4003. 5 indexed citations
17.
Liu, Xin, Shaoxin Li, Meina Kan, et al.. (2015). AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation. 258–266. 100 indexed citations
18.
Li, Shaoxin, Junliang Xing, Zhiheng Niu, Shiguang Shan, & Shuicheng Yan. (2015). Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition. 222–230. 33 indexed citations
19.
Li, Shaoxin & Shiguang Shan. (2011). Margin Emphasized Metric Learning and its application to Gabor feature based face recognition. 15. 579–584. 5 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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