Pengfei Ge

544 total citations
10 papers, 387 citations indexed

About

Pengfei Ge is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Infectious Diseases. According to data from OpenAlex, Pengfei Ge has authored 10 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 1 paper in Infectious Diseases. Recurrent topics in Pengfei Ge's work include Domain Adaptation and Few-Shot Learning (7 papers), Machine Learning and ELM (3 papers) and Video Surveillance and Tracking Methods (3 papers). Pengfei Ge is often cited by papers focused on Domain Adaptation and Few-Shot Learning (7 papers), Machine Learning and ELM (3 papers) and Video Surveillance and Tracking Methods (3 papers). Pengfei Ge collaborates with scholars based in China, Singapore and Macao. Pengfei Ge's co-authors include Chuan-Xian Ren, Yiming Zhai, You-Wei Luo, Mengxue Li, Shuicheng Yan, Xiaolin Xu, Hong Yan, Dao‐Qing Dai, Jiashi Feng and Zhen Lei and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Pengfei Ge

10 papers receiving 385 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pengfei Ge China	9	280	240	35	34	24	10	387
You-Wei Luo China	7	295 1.1×	206 0.9×	36 1.0×	43 1.3×	15 0.6×	14	350
Pau Panareda Busto Germany	4	334 1.2×	290 1.2×	38 1.1×	43 1.3×	31 1.3×	5	444
Shuhao Cui China	5	380 1.4×	277 1.2×	53 1.5×	41 1.2×	28 1.2×	9	484
Hongzu Su China	8	261 0.9×	177 0.7×	32 0.9×	28 0.8×	11 0.5×	15	331
Dapeng Hu China	5	295 1.1×	224 0.9×	40 1.1×	28 0.8×	20 0.8×	6	382
Limin Su China	5	226 0.8×	135 0.6×	23 0.7×	25 0.7×	27 1.1×	13	293
Fabio Maria Carlucci Italy	8	321 1.1×	294 1.2×	72 2.1×	26 0.8×	11 0.5×	11	410
Qianfen Jiao China	6	312 1.1×	298 1.2×	53 1.5×	33 1.0×	10 0.4×	12	438
Debasmit Das United Kingdom	7	170 0.6×	135 0.6×	28 0.8×	18 0.5×	25 1.0×	19	303
Seonguk Seo South Korea	7	187 0.7×	237 1.0×	46 1.3×	13 0.4×	10 0.4×	13	407

Countries citing papers authored by Pengfei Ge

Since Specialization

Citations

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

Fields of papers citing papers by Pengfei Ge

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pengfei Ge

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Yu, Yu‐Feng, et al.. (2024). Multi-Scale Enhanced Features Correlation Filters Learning With Dual Second-Order Difference for UAV Tracking. IEEE Transactions on Intelligent Vehicles. 9(2). 3232–3245. 9 indexed citations

2.

Ge, Pengfei, et al.. (2023). Gaussian Process-Based Transfer Kernel Learning for Unsupervised Domain Adaptation. Mathematics. 11(22). 4695–4695. 4 indexed citations

3.

Ge, Pengfei, Chuan-Xian Ren, Xiaolin Xu, & Hong Yan. (2022). Unsupervised Domain Adaptation via Deep Conditional Adaptation Network. Pattern Recognition. 134. 109088–109088. 57 indexed citations

4.

Ren, Chuan-Xian, et al.. (2020). Learning Target-Domain-Specific Classifier for Partial Domain Adaptation. IEEE Transactions on Neural Networks and Learning Systems. 32(5). 1989–2001. 28 indexed citations

5.

Luo, You-Wei, Chuan-Xian Ren, Pengfei Ge, Ke-Kun Huang, & Yu‐Feng Yu. (2020). Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5029–5036. 26 indexed citations

6.

Li, Mengxue, Yiming Zhai, You-Wei Luo, Pengfei Ge, & Chuan-Xian Ren. (2020). Enhanced Transport Distance for Unsupervised Domain Adaptation. 13933–13941. 149 indexed citations

7.

Ren, Chuan-Xian, Pengfei Ge, Dao‐Qing Dai, & Hong Yan. (2019). Learning Kernel for Conditional Moment-Matching Discrepancy-Based Image Classification. IEEE Transactions on Cybernetics. 51(4). 2006–2018. 16 indexed citations

8.

Ren, Chuan-Xian, et al.. (2019). Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation. IEEE Transactions on Information Forensics and Security. 15. 1290–1302. 44 indexed citations

9.

Ge, Pengfei, Chuan-Xian Ren, Dao‐Qing Dai, Jiashi Feng, & Shuicheng Yan. (2019). Dual Adversarial Autoencoders for Clustering. IEEE Transactions on Neural Networks and Learning Systems. 31(4). 1417–1424. 46 indexed citations

10.

Ren, Chuan-Xian, et al.. (2019). Deep metric learning via subtype fuzzy clustering. Pattern Recognition. 90. 210–219. 8 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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