Meina Kan

6.0k total citations · 3 hit papers
52 papers, 3.2k citations indexed

About

Meina Kan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Meina Kan has authored 52 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Computer Vision and Pattern Recognition, 17 papers in Artificial Intelligence and 15 papers in Signal Processing. Recurrent topics in Meina Kan's work include Face recognition and analysis (32 papers), Face and Expression Recognition (23 papers) and Domain Adaptation and Few-Shot Learning (15 papers). Meina Kan is often cited by papers focused on Face recognition and analysis (32 papers), Face and Expression Recognition (23 papers) and Domain Adaptation and Few-Shot Learning (15 papers). Meina Kan collaborates with scholars based in China, Singapore and France. Meina Kan's co-authors include Shiguang Shan, Xilin Chen, Yude Wang, Wangmeng Zuo, Jie Zhang, Shihong Lao, Haihong Zhang, Jie Zhang, Shuzhe Wu and Hong Chang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Meina Kan

51 papers receiving 3.1k citations

Hit Papers

AttGAN: Facial Attribute Editing by Only Changing What ... 2015 2026 2018 2022 2019 2020 2015 100 200 300 400
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. AttGAN: Facial Attribute Editing by Only Changing What You Want
    2019 475 citations Wangmeng Zuo, Meina Kan et al. IEEE Transactions on Image Processing profile →
  2. Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
    2020 473 citations Yude Wang, Jie Zhang et al. profile →
  3. Multi-View Discriminant Analysis
    2015 452 citations Meina Kan, Shiguang Shan 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
Meina Kan China 25 2.6k 1.1k 544 233 133 52 3.2k
Changxing Ding China 24 2.3k 0.9× 514 0.5× 529 1.0× 123 0.5× 82 0.6× 52 2.6k
Hélio Pedrini Brazil 25 1.8k 0.7× 461 0.4× 768 1.4× 325 1.4× 84 0.6× 200 2.7k
Lu Jiang United States 28 2.5k 1.0× 1.9k 1.8× 207 0.4× 163 0.7× 137 1.0× 63 3.7k
Dong Chen China 20 2.5k 0.9× 784 0.7× 355 0.7× 176 0.8× 149 1.1× 78 3.2k
Jean‐Luc Dugelay France 29 3.6k 1.4× 573 0.5× 1.2k 2.2× 245 1.1× 74 0.6× 226 4.1k
Weidi Xie United Kingdom 24 2.5k 0.9× 1.4k 1.3× 1.4k 2.5× 142 0.6× 269 2.0× 62 4.0k
Yitong Wang China 14 1.7k 0.6× 678 0.6× 654 1.2× 127 0.5× 80 0.6× 48 2.3k
Yandong Wen China 12 2.1k 0.8× 720 0.7× 949 1.7× 83 0.4× 81 0.6× 32 2.6k
Zuxuan Wu China 28 2.3k 0.9× 1.4k 1.3× 178 0.3× 158 0.7× 71 0.5× 82 3.0k
Jonathan Brandt United States 17 1.9k 0.7× 601 0.6× 342 0.6× 111 0.5× 103 0.8× 32 2.4k

Countries citing papers authored by Meina Kan

Since Specialization
Citations

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

Fields of papers citing papers by Meina Kan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meina Kan

This figure shows the co-authorship network connecting the top 25 collaborators of Meina Kan. A scholar is included among the top collaborators of Meina Kan 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 Meina Kan. Meina Kan 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.
Kan, Meina, Lixuan Zhang, Liang Fan, et al.. (2025). eLabrador: A Wearable Navigation System for Visually Impaired Individuals. IEEE Transactions on Automation Science and Engineering. 22. 12228–12244. 1 indexed citations
2.
Tang, Xiaolong, Meina Kan, Shiguang Shan, et al.. (2024). HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention. 15261–15270. 24 indexed citations
3.
4.
Wang, Yude, Jie Zhang, Meina Kan, Shiguang Shan, & Xilin Chen. (2023). BLPSeg: Balance the Label Preference in Scribble-Supervised Semantic Segmentation. IEEE Transactions on Image Processing. 32. 4921–4934. 7 indexed citations
5.
Shan, Shiguang, et al.. (2022). Personalized Convolution for Face Recognition. International Journal of Computer Vision. 130(2). 344–362. 8 indexed citations
6.
Sun, Zhenan, Ran He, Liang Wang, et al.. (2021). 生物特征识别学科发展报告. Journal of Image and Graphics. 26(6). 1254–1329. 6 indexed citations
7.
Wang, Yude, Jie Zhang, Meina Kan, Shiguang Shan, & Xilin Chen. (2020). Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation. 12272–12281. 473 indexed citations breakdown →
8.
Kan, Meina, et al.. (2020). Learning to Learn Adaptive Classifier–Predictor for Few-Shot Learning. IEEE Transactions on Neural Networks and Learning Systems. 32(8). 3458–3470. 97 indexed citations
9.
Kan, Meina, et al.. (2020). Learning deep face representation with long-tail data: An aggregate-and-disperse approach. Pattern Recognition Letters. 133. 48–54. 6 indexed citations
10.
Li, Xiaoyan, Meina Kan, Shiguang Shan, & Xilin Chen. (2019). Weakly Supervised Object Detection With Segmentation Collaboration. 9734–9743. 74 indexed citations
11.
Zhang, Jie, et al.. (2019). Deformable face net for pose invariant face recognition. Pattern Recognition. 100. 107113–107113. 52 indexed citations
12.
Zuo, Wangmeng, et al.. (2019). AttGAN: Facial Attribute Editing by Only Changing What You Want. IEEE Transactions on Image Processing. 28(11). 5464–5478. 475 indexed citations breakdown →
13.
Wu, Shuzhe, Meina Kan, Shiguang Shan, & Xilin Chen. (2019). Hierarchical Attention for Part-Aware Face Detection. International Journal of Computer Vision. 127(6-7). 560–578. 23 indexed citations
14.
Kan, Meina, et al.. (2018). Face Anti-Spoofing with Multi-Scale Information. 9 indexed citations
15.
Li, Yong, Jiabei Zeng, Jie Zhang, et al.. (2017). KinNet. 13–20. 27 indexed citations
16.
Kan, Meina, et al.. (2017). LDF-Net: Learning a Displacement Field Network for Face Recognition across Pose. 9–16. 14 indexed citations
17.
Wu, Shuzhe, Meina Kan, Zhenliang He, Shiguang Shan, & Xilin Chen. (2016). Funnel-structured cascade for multi-view face detection with alignment-awareness. Neurocomputing. 221. 138–145. 81 indexed citations
18.
Zhang, Jie, Meina Kan, Shiguang Shan, & Xilin Chen. (2016). Occlusion-Free Face Alignment: Deep Regression Networks Coupled with De-Corrupt AutoEncoders. 3428–3437. 65 indexed citations
19.
Kan, Meina, Shiguang Shan, & Xilin Chen. (2015). Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation. 3846–3854. 40 indexed citations
20.
Kan, Meina, Shiguang Shan, Dong Xu, & Xilin Chen. (2011). Side-Information based Linear Discriminant Analysis for Face Recognition. 125.1–125.0. 59 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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