Yehui Tang

3.2k total citations · 1 hit paper
19 papers, 1.4k citations indexed

About

Yehui Tang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Yehui Tang has authored 19 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 2 papers in Aerospace Engineering. Recurrent topics in Yehui Tang's work include Advanced Neural Network Applications (15 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Image and Video Retrieval Techniques (6 papers). Yehui Tang is often cited by papers focused on Advanced Neural Network Applications (15 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Image and Video Retrieval Techniques (6 papers). Yehui Tang collaborates with scholars based in China, Sweden and Australia. Yehui Tang's co-authors include Yunhe Wang, Chang Xu, Kai Han, Jianyuan Guo, Xinghao Chen, Han Wu, Yixing Xu, Chao Xu, Dacheng Tao and Chao Xu and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and arXiv (Cornell University).

In The Last Decade

Yehui Tang

19 papers receiving 1.3k citations

Hit Papers

CMT: Convolutional Neural Networks Meet Vision Transformers 2022 2026 2023 2024 2022 100 200 300 400 500
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. CMT: Convolutional Neural Networks Meet Vision Transformers
    2022 569 citations Jianyuan Guo, Kai Han et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yehui Tang China 15 862 491 199 155 101 19 1.4k
Zihang Jiang China 11 1.2k 1.4× 570 1.2× 263 1.3× 194 1.3× 132 1.3× 23 1.9k
Chun-Fu Richard Chen United States 6 797 0.9× 395 0.8× 199 1.0× 107 0.7× 104 1.0× 12 1.4k
Fengwei Yu China 11 822 1.0× 534 1.1× 136 0.7× 165 1.1× 73 0.7× 18 1.2k
Quan Zhou China 22 1.1k 1.3× 537 1.1× 174 0.9× 148 1.0× 144 1.4× 102 1.7k
Xiaoyi Dong China 11 1.0k 1.2× 504 1.0× 252 1.3× 161 1.0× 136 1.3× 26 1.6k
Xiaoheng Jiang China 16 847 1.0× 517 1.1× 114 0.6× 76 0.5× 64 0.6× 52 1.4k
Yizeng Han China 12 559 0.6× 361 0.7× 113 0.6× 141 0.9× 80 0.8× 22 1.0k
Qinghe Zheng China 17 477 0.6× 423 0.9× 106 0.5× 162 1.0× 121 1.2× 39 1.3k
Peishu Wu China 18 572 0.7× 442 0.9× 94 0.5× 110 0.7× 50 0.5× 39 1.4k
Junshi Huang China 14 1.5k 1.8× 729 1.5× 169 0.8× 66 0.4× 82 0.8× 25 2.0k

Countries citing papers authored by Yehui Tang

Since Specialization
Citations

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

Fields of papers citing papers by Yehui Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yehui Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Yehui Tang. A scholar is included among the top collaborators of Yehui 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 Yehui Tang. Yehui Tang 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.
Tang, Yehui, et al.. (2025). TinySAM: Pushing the Envelope for Efficient Segment Anything Model. Proceedings of the AAAI Conference on Artificial Intelligence. 39(19). 20470–20478. 4 indexed citations
2.
3.
Wang, Haoqing, Yehui Tang, Yunhe Wang, et al.. (2023). Masked Image Modeling with Local Multi-Scale Reconstruction. 2122–2131. 30 indexed citations
4.
Tang, Yehui, et al.. (2023). Network Expansion For Practical Training Acceleration. 20269–20279. 3 indexed citations
5.
Guo, Jianyuan, Kai Han, Han Wu, et al.. (2022). CMT: Convolutional Neural Networks Meet Vision Transformers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12165–12175. 569 indexed citations breakdown →
6.
Tang, Yehui, Kai Han, Jianyuan Guo, et al.. (2022). An Image Patch is a Wave: Phase-Aware Vision MLP. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10925–10934. 85 indexed citations
7.
Guo, Jianyuan, Yehui Tang, Kai Han, et al.. (2022). Hire-MLP: Vision MLP via Hierarchical Rearrangement. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 816–826. 53 indexed citations
8.
Tang, Yehui, Kai Han, Yunhe Wang, et al.. (2022). Patch Slimming for Efficient Vision Transformers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12155–12164. 111 indexed citations
9.
Ding, Ning, Yixing Xu, Yehui Tang, et al.. (2022). Source-Free Domain Adaptation via Distribution Estimation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7202–7212. 87 indexed citations
10.
Zhu, Mingjian, Kai Han, Yehui Tang, & Yunhe Wang. (2021). Visual Transformer Pruning.. arXiv (Cornell University). 15 indexed citations
11.
Tang, Yehui, et al.. (2021). Homogeneous Architecture Augmentation for Neural Predictor. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 12229–12238. 23 indexed citations
12.
Xu, Yixing, Yunhe Wang, Kai Han, et al.. (2021). ReNAS: Relativistic Evaluation of Neural Architecture Search. 4409–4418. 52 indexed citations
13.
Tang, Yehui, Yunhe Wang, Yixing Xu, et al.. (2021). Manifold Regularized Dynamic Network Pruning. 5016–5026. 62 indexed citations
14.
Tang, Yehui, Yunhe Wang, Yixing Xu, et al.. (2020). SCOP: Scientific Control for Reliable Neural Network Pruning. Neural Information Processing Systems. 33. 10936–10947. 11 indexed citations
15.
Han, Jin, Chu Zhou, Peiqi Duan, et al.. (2020). Neuromorphic Camera Guided High Dynamic Range Imaging. 1727–1736. 64 indexed citations
16.
Han, Kai, Yunhe Wang, Hanting Chen, et al.. (2020). A Survey on Visual Transformer. arXiv (Cornell University). 150 indexed citations
17.
Tang, Yehui, Shan You, Chang Xu, et al.. (2020). Reborn Filters: Pruning Convolutional Neural Networks with Limited Data. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5972–5980. 17 indexed citations
18.
Chen, Hanting, Yunhe Wang, Yehui Tang, et al.. (2020). Frequency Domain Compact 3D Convolutional Neural Networks. 1638–1647. 17 indexed citations
19.
Tang, Yehui, Yunhe Wang, Yixing Xu, et al.. (2020). Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5964–5971. 24 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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