Jingru Tan

1.0k total citations · 1 hit paper
10 papers, 598 citations indexed

About

Jingru Tan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology. According to data from OpenAlex, Jingru Tan has authored 10 papers receiving a total of 598 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 1 paper in Neurology. Recurrent topics in Jingru Tan's work include Advanced Neural Network Applications (7 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Jingru Tan is often cited by papers focused on Advanced Neural Network Applications (7 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Jingru Tan collaborates with scholars based in China, Australia and United States. Jingru Tan's co-authors include Quanquan Li, Changqing Yin, Wanli Ouyang, Junjie Yan, Buyu Li, Changbao Wang, Xin Lu, Gang Zhang, Ye Luo and Jianwei Lu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neural Networks and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Jingru Tan

9 papers receiving 588 citations

Hit Papers

Equalization Loss for Long-Tailed Object Recognition 2020 2026 2022 2024 2020 50 100 150 200 250
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. Equalization Loss for Long-Tailed Object Recognition
    2020 281 citations Jingru Tan, Changbao 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
Jingru Tan China 6 349 335 67 63 30 10 598
Quanquan Li China 5 386 1.1× 304 0.9× 56 0.8× 48 0.8× 25 0.8× 9 612
Zhisheng Zhong China 8 515 1.5× 365 1.1× 96 1.4× 84 1.3× 42 1.4× 8 761
Wei Zhai China 14 382 1.1× 335 1.0× 55 0.8× 44 0.7× 31 1.0× 42 604
Jianzhong He China 8 406 1.2× 250 0.7× 95 1.4× 68 1.1× 50 1.7× 10 585
Huan Xiong China 12 386 1.1× 233 0.7× 45 0.7× 54 0.9× 23 0.8× 57 664
Francisco Massa 2 368 1.1× 204 0.6× 91 1.4× 67 1.1× 26 0.9× 2 658
Sivan Harary Israel 10 366 1.0× 307 0.9× 60 0.9× 48 0.8× 33 1.1× 10 508
Yousong Zhu China 8 400 1.1× 170 0.5× 111 1.7× 42 0.7× 24 0.8× 17 535
Golnaz Ghiasi United States 10 512 1.5× 227 0.7× 59 0.9× 54 0.9× 19 0.6× 13 629

Countries citing papers authored by Jingru Tan

Since Specialization
Citations

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

Fields of papers citing papers by Jingru Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jingru Tan

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

All Works

10 of 10 papers shown
1.
Li, Bo, Yongqiang Yao, Jingru Tan, et al.. (2024). Robust long-tailed recognition with distribution-aware adversarial example generation. Neural Networks. 184. 106932–106932.
2.
Li, Bo, Yongqiang Yao, Jingru Tan, et al.. (2024). Rectify representation bias in vision-language models for long-tailed recognition. Neural Networks. 172. 106134–106134. 5 indexed citations
3.
Li, Yong–Lu, Zehao Wang, Yiming Dou, et al.. (2024). From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding. 16582–16592. 1 indexed citations
4.
Tan, Jingru, Bo Li, Yongqiang Yao, et al.. (2023). The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(11). 13876–13892. 15 indexed citations
5.
Li, Bo, Yongqiang Yao, Jingru Tan, et al.. (2022). Equalized Focal Loss for Dense Long-Tailed Object Detection. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 6980–6989. 86 indexed citations
6.
Zhang, Gang, Xin Lu, Jingru Tan, et al.. (2021). RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features. 6857–6865. 91 indexed citations
7.
Tan, Jingru, Xin Lu, Gang Zhang, Changqing Yin, & Quanquan Li. (2021). Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection. 1685–1694. 106 indexed citations
8.
Zhou, Pei‐Yun, Jingru Tan, Bao‐Ye Sun, et al.. (2021). Liver Tumor Detection Via A Multi-Scale Intermediate Multi-Modal Fusion Network on MRI Images. 299–303. 12 indexed citations
9.
Tan, Jingru, Changbao Wang, Buyu Li, et al.. (2020). Equalization Loss for Long-Tailed Object Recognition. 11659–11668. 281 indexed citations breakdown →
10.

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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2026