Ruimao Zhang

4.8k total citations · 2 hit papers
72 papers, 2.1k citations indexed

About

Ruimao Zhang is a scholar working on Computer Vision and Pattern Recognition, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Ruimao Zhang has authored 72 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Computer Vision and Pattern Recognition, 20 papers in Materials Chemistry and 17 papers in Artificial Intelligence. Recurrent topics in Ruimao Zhang's work include Advanced Neural Network Applications (16 papers), Advanced Image and Video Retrieval Techniques (16 papers) and Multimodal Machine Learning Applications (12 papers). Ruimao Zhang is often cited by papers focused on Advanced Neural Network Applications (16 papers), Advanced Image and Video Retrieval Techniques (16 papers) and Multimodal Machine Learning Applications (12 papers). Ruimao Zhang collaborates with scholars based in China, Hong Kong and Germany. Ruimao Zhang's co-authors include Ping Luo, Liang Lin, Dongyu Zhang, Ya Li, Keze Wang, Dongju Zhang, Yuying Ge, Xiaogang Wang, Wei Liu and Xiaoou Tang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Chemical Physics Letters.

In The Last Decade

Ruimao Zhang

70 papers receiving 2.0k citations

Hit Papers

Cost-Effective Active Learning for Deep Image Classification 2016 2026 2019 2022 2016 2021 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ruimao Zhang China 19 1.2k 533 336 276 139 72 2.1k
Guohao Li China 18 577 0.5× 643 1.2× 532 1.6× 313 1.1× 201 1.4× 65 2.1k
Shuai Li China 26 1.2k 1.1× 304 0.6× 118 0.4× 152 0.6× 202 1.5× 132 2.1k
Chaoyue Wang China 22 1.1k 0.9× 367 0.7× 190 0.6× 84 0.3× 70 0.5× 79 2.2k
Lin Wang China 23 554 0.5× 337 0.6× 341 1.0× 67 0.2× 127 0.9× 164 1.9k
Wenming Yang China 23 1.8k 1.5× 299 0.6× 156 0.5× 167 0.6× 340 2.4× 189 2.8k
Yixuan Li China 21 865 0.7× 290 0.5× 80 0.2× 89 0.3× 216 1.6× 86 1.8k
Zunlei Feng China 14 762 0.7× 281 0.5× 63 0.2× 67 0.2× 59 0.4× 83 1.3k
Takahiro Okabe Japan 28 1.6k 1.4× 150 0.3× 104 0.3× 124 0.4× 482 3.5× 138 2.8k
Xirong Li China 32 2.4k 2.1× 770 1.4× 111 0.3× 67 0.2× 72 0.5× 119 3.3k
Ming‐Ching Chang United States 20 1.3k 1.1× 510 1.0× 47 0.1× 58 0.2× 114 0.8× 108 1.7k

Countries citing papers authored by Ruimao Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Ruimao Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruimao Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Ruimao Zhang. A scholar is included among the top collaborators of Ruimao Zhang 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 Ruimao Zhang. Ruimao Zhang 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.
Wang, Chaoqun, et al.. (2025). Boosting 3D Object Detection via Self-Distilling Introspective Data. IEEE Transactions on Intelligent Transportation Systems. 26(5). 6587–6600.
2.
Yang, Jie, Ailing Zeng, Tianhe Ren, et al.. (2025). ED-Pose++: Enhanced Explicit Box Detection for Conventional and Interactive Multi-Object Keypoint Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(7). 5636–5654. 1 indexed citations
3.
Liu, Yuru, Klement Foo, Ruimao Zhang, et al.. (2024). Regio-MPNN: predicting regioselectivity for general metal-catalyzed cross-coupling reactions using a chemical knowledge informed message passing neural network. Digital Discovery. 3(10). 2019–2031. 1 indexed citations
4.
Liu, Ziyang, Ruimao Zhang, Rui Zhu, et al.. (2024). Carbon quantum dots (CQDs) modified α-Fe2O3/BiVO4 heterojunction photoanode for enhancing photoelectrochemical water splitting. Electrochimica Acta. 514. 145621–145621. 10 indexed citations
5.
Yin, Zhenfei, et al.. (2024). MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception. 16307–16316. 5 indexed citations
6.
Xie, Liangbin, Xintao Wang, Ziyang Yuan, et al.. (2024). SmartEdit: Exploring Complex Instruction-Based Image Editing with Multimodal Large Language Models. 8362–8371. 12 indexed citations
7.
Yang, Jie, et al.. (2023). Neural Interactive Keypoint Detection. 15076–15086. 9 indexed citations
8.
9.
Luo, Ping, et al.. (2019). Differentiable Dynamic Normalization for Learning Deep Representation. International Conference on Machine Learning. 4203–4211. 11 indexed citations
10.
Zhang, Zhaoyang, Jingyu Li, Wenqi Shao, et al.. (2019). Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks. 3541–3550. 31 indexed citations
11.
Ge, Yuying, Ruimao Zhang, Xiaogang Wang, Xiaoou Tang, & Ping Luo. (2019). DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images. 5332–5340. 182 indexed citations
12.
Zhang, Ruimao, et al.. (2018). Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(3). 596–610. 17 indexed citations
13.
Luo, Ping, et al.. (2018). Differentiable Learning-to-Normalize via Switchable Normalization. International Conference on Learning Representations. 12 indexed citations
14.
Lin, Di, Ruimao Zhang, Yuanfeng Ji, Ping Li, & Hui Huang. (2018). SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images. IEEE Transactions on Cybernetics. 50(3). 1120–1131. 68 indexed citations
15.
Zhang, Ruimao, et al.. (2017). Scene Parsing by Weakly Supervised Learning with Image Descriptions.. arXiv (Cornell University). 1 indexed citations
16.
Zhang, Dongyu, et al.. (2017). Image-to-Video Person Re-Identification With Temporally Memorized Similarity Learning. IEEE Transactions on Circuits and Systems for Video Technology. 28(10). 2622–2632. 31 indexed citations
17.
Peng, Zhanglin, Ruimao Zhang, Xiaodan Liang, Xiaobai Liu, & Liang Lin. (2016). Geometric scene parsing with hierarchical LSTM. International Joint Conference on Artificial Intelligence. 3439–3445. 1 indexed citations
18.
Fan, Wei, Andréia Luísa da Rosa, Thomas Frauenheim, & Ruimao Zhang. (2008). Energetic and electronic properties of hydrogen passivated ZnO nanowires. Solid State Communications. 148(3-4). 101–104. 7 indexed citations
19.
Guo, Chen, Wenjie Fan, & Ruimao Zhang. (2006). Influence of sublayer atoms on Si(100) surface reconstructions. Solid State Communications. 137(10). 553–556. 1 indexed citations
20.
Zhang, Ruimao, J.L. Andújar, & E. Bertrán. (1997). Modeling interface structures of cubic boron nitride films deposited heteroepitaxially and via a hexagonal boron nitride interlayer on silicon (001) surfaces. Diamond and Related Materials. 6(5-7). 589–593. 3 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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