Beier Zhu

536 total citations · 1 hit paper
11 papers, 309 citations indexed

About

Beier Zhu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Beier Zhu has authored 11 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 3 papers in Control and Systems Engineering. Recurrent topics in Beier Zhu's work include Multimodal Machine Learning Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Power Systems Fault Detection (3 papers). Beier Zhu is often cited by papers focused on Multimodal Machine Learning Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Power Systems Fault Detection (3 papers). Beier Zhu collaborates with scholars based in Singapore, China and Cayman Islands. Beier Zhu's co-authors include Hanwang Zhang, Yulei Niu, Xinzhou Dong, Yue Wu, Shenxing Shi, Shenxing Shi, Sohrab Mirsaeidi, Xian‐Sheng Hua, Renjie Liao and Jiwen Lu and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Transactions on Smart Grid and Frontiers of Computer Science.

In The Last Decade

Beier Zhu

9 papers receiving 295 citations

Hit Papers

Prompt-aligned Gradient for Prompt Tuning 2023 2026 2024 2025 2023 25 50 75 100
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. Prompt-aligned Gradient for Prompt Tuning
    2023 119 citations Beier Zhu, Yulei Niu et al. profile →

Peers

Beier Zhu
Haider K. Hoomod Iraq
Mohamed Masoud United States
Jiazhen Lin China
Wenlin Wang China
Bo Xie China
Jongheon Jeong South Korea
Peng Cao China
Naser Tarhuni Oman
Haider K. Hoomod Iraq
Beier Zhu
Citations per year, relative to Beier Zhu Beier Zhu (= 1×) peers Haider K. Hoomod

Countries citing papers authored by Beier Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Beier Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Beier Zhu

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

All Works

11 of 11 papers shown
1.
2.
Zhu, Beier & Hanwang Zhang. (2024). Debiasing vision-language models for vision tasks: a survey. Frontiers of Computer Science. 19(1). 2 indexed citations
3.
Zhu, Beier, et al.. (2024). Selective Vision-Language Subspace Projection for Few-shot CLIP. 3848–3857.
4.
Zhu, Beier, et al.. (2023). Debiased Fine-Tuning for Vision-Language Models by Prompt Regularization. Proceedings of the AAAI Conference on Artificial Intelligence. 37(3). 3834–3842. 10 indexed citations
5.
Chen, Chen, et al.. (2023). Leveraging Modality-Specific Representations for Audio-Visual Speech Recognition via Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 12607–12615. 13 indexed citations
6.
Zhu, Beier, et al.. (2023). Prompt-aligned Gradient for Prompt Tuning. 15613–15623. 119 indexed citations breakdown →
7.
Zhu, Beier, Yulei Niu, Xian‐Sheng Hua, & Hanwang Zhang. (2022). Cross-Domain Empirical Risk Minimization for Unbiased Long-Tailed Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 36(3). 3589–3597. 23 indexed citations
8.
Lin, Chunze, Beier Zhu, Quan Wang, et al.. (2021). Structure-Coherent Deep Feature Learning for Robust Face Alignment. IEEE Transactions on Image Processing. 30. 5313–5326. 21 indexed citations
9.
Shi, Shenxing, et al.. (2019). Fault Location for Radial Distribution Network via Topology and Reclosure-Generating Traveling Waves. IEEE Transactions on Smart Grid. 10(6). 6404–6413. 79 indexed citations
10.
Zhu, Beier, et al.. (2018). Fault location in AC transmission lines with back‐to‐back MMC‐HVDC using ConvNets. The Journal of Engineering. 2019(16). 2430–2434. 11 indexed citations
11.
Shi, Shenxing, Beier Zhu, Sohrab Mirsaeidi, & Xinzhou Dong. (2018). Fault Classification for Transmission Lines Based on Group Sparse Representation. IEEE Transactions on Smart Grid. 10(4). 4673–4682. 31 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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