Aojun Zhou

2.9k total citations · 1 hit paper
25 papers, 748 citations indexed

About

Aojun Zhou is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Aojun Zhou has authored 25 papers receiving a total of 748 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 4 papers in Information Systems. Recurrent topics in Aojun Zhou's work include Advanced Neural Network Applications (13 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Image and Video Retrieval Techniques (5 papers). Aojun Zhou is often cited by papers focused on Advanced Neural Network Applications (13 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Image and Video Retrieval Techniques (5 papers). Aojun Zhou collaborates with scholars based in China, Hong Kong and United States. Aojun Zhou's co-authors include Kun Yuan, Ziwei Liu, Fengwei Yu, Shaopeng Guo, Wei Wu, Anbang Yao, Zhongfeng Wang, Chao Fang, Hongkai Xiong and Yongzhuang Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Multimedia and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Aojun Zhou

21 papers receiving 731 citations

Hit Papers

Incorporating Convolution Designs into Visual Transformers 2021 2026 2022 2024 2021 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. Incorporating Convolution Designs into Visual Transformers
    2021 373 citations Aojun Zhou 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
Aojun Zhou China 10 509 298 103 92 56 25 748
Yizeng Han China 12 559 1.1× 361 1.2× 141 1.4× 113 1.2× 80 1.4× 22 1.0k
Fengwei Yu China 11 822 1.6× 534 1.8× 165 1.6× 136 1.5× 73 1.3× 18 1.2k
Kun Yuan China 12 500 1.0× 222 0.7× 55 0.5× 121 1.3× 42 0.8× 32 727
Hao Shen China 12 550 1.1× 176 0.6× 88 0.9× 61 0.7× 74 1.3× 44 856
Alaaeldin El-Nouby Sweden 4 438 0.9× 352 1.2× 49 0.5× 52 0.6× 49 0.9× 6 835
Ruihao Gong China 14 751 1.5× 666 2.2× 257 2.5× 76 0.8× 50 0.9× 34 1.2k
S. Srinivas Kumar India 16 623 1.2× 149 0.5× 70 0.7× 182 2.0× 44 0.8× 77 919
Zhen Dong China 17 732 1.4× 408 1.4× 366 3.6× 192 2.1× 52 0.9× 43 1.2k
Sung‐Ho Bae South Korea 19 762 1.5× 160 0.5× 106 1.0× 176 1.9× 35 0.6× 75 1.2k

Countries citing papers authored by Aojun Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Aojun Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aojun Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Aojun Zhou. A scholar is included among the top collaborators of Aojun Zhou 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 Aojun Zhou. Aojun Zhou 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.
2.
Xie, Y. H., Yinghao Chen, Renyu Hu, et al.. (2025). Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding. 29558–29568.
3.
Wang, Ke, Junting Pan, Aojun Zhou, et al.. (2025). MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning. 2505–2534.
4.
5.
Fang, Rongyao, Peng Gao, Aojun Zhou, et al.. (2024). FeatAug-DETR: Enriching One-to-Many Matching for DETRs With Feature Augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(9). 6402–6415. 8 indexed citations
6.
Lü, Xudong, Qi Liu, Yuhui Xu, et al.. (2024). Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models. 6159–6172. 5 indexed citations
7.
Wang, Jian‐Sheng, et al.. (2024). Large Language Models Augmented Rating Prediction in Recommender System. 7960–7964. 3 indexed citations
8.
Liu, Jianbo, Xiaolin Zhang, Maoqing Tian, et al.. (2024). Pyramid Fusion Transformer for Semantic Segmentation. IEEE Transactions on Multimedia. 26. 9630–9643. 5 indexed citations
9.
Li, Hongsheng, et al.. (2024). Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset. 95095–95169.
10.
Fang, Chao, Wei Sun, Aojun Zhou, & Zhongfeng Wang. (2023). CEST: Computation-Efficient N:M Sparse Training for Deep Neural Networks. 1–2. 2 indexed citations
11.
Fang, Chao, et al.. (2023). Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 43(2). 506–519. 9 indexed citations
12.
Zhu, Xiangyang, Bowei He, Aojun Zhou, et al.. (2023). Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement. 2605–2615. 27 indexed citations
13.
Zhou, Aojun, Li Yang, Zipeng Qin, et al.. (2023). SparseMAE: Sparse Training Meets Masked Autoencoders. 16130–16140. 4 indexed citations
14.
Zhang, Shilong, et al.. (2022). Group R-CNN for Weakly Semi-supervised Object Detection with Points. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9407–9416. 31 indexed citations
15.
Sun, Wei, Aojun Zhou, Sander Stuijk, et al.. (2021). DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks. Neural Information Processing Systems. 34. 11 indexed citations
16.
Zhou, Aojun, et al.. (2019). Towards Improving Generalization of Deep Networks via Consistent Normalization.. arXiv (Cornell University). 2 indexed citations
17.
Li, Duo, Aojun Zhou, & Anbang Yao. (2019). HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions. 3315–3324. 21 indexed citations
18.
Zhou, Aojun, Anbang Yao, Kuan Wang, & Yurong Chen. (2018). Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks. 9426–9435. 50 indexed citations
19.
Zhou, Aojun, Yue Cui, & Tianzi Jiang. (2018). Multisite Schizophrenia Classification Based on Brainnetome Atlas by Deep Learning. 451–455. 4 indexed citations
20.
Xu, Yuhui, Yongzhuang Wang, Aojun Zhou, Weiyao Lin, & Hongkai Xiong. (2018). Deep Neural Network Compression With Single and Multiple Level Quantization. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 85 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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