Shipeng Yan

1.2k total citations · 1 hit paper
8 papers, 623 citations indexed

About

Shipeng Yan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Shipeng Yan has authored 8 papers receiving a total of 623 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Shipeng Yan's work include Domain Adaptation and Few-Shot Learning (6 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Multimodal Machine Learning Applications (3 papers). Shipeng Yan is often cited by papers focused on Domain Adaptation and Few-Shot Learning (6 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Multimodal Machine Learning Applications (3 papers). Shipeng Yan collaborates with scholars based in China, United Kingdom and United States. Shipeng Yan's co-authors include Xuming He, Jiangwei Xie, Songyang Zhang, Jian Sun, Zeming Li, Shuaiyi Huang, Qiuyue Wang, Jiale Zhou, Songyang Zhang and Can Li and has published in prestigious journals such as Journal of Cancer, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and International Conference on Machine Learning.

In The Last Decade

Shipeng Yan

7 papers receiving 615 citations

Hit Papers

align trajectories

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.

DER: Dynamically Expandable Representation for Class Incremental Learning

2021 312 citations Shipeng Yan, Jiangwei Xie et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shipeng Yan China	7	462	357	79	33	21	8	623
Massimiliano Mancini Italy	11	438 0.9×	292 0.8×	105 1.3×	35 1.1×	27 1.3×	29	607
Songlin Dong China	9	392 0.8×	232 0.6×	59 0.7×	23 0.7×	20 1.0×	19	482
Minghao Xu China	9	493 1.1×	483 1.4×	92 1.2×	34 1.0×	19 0.9×	11	675
Zheda Mai Canada	8	428 0.9×	272 0.8×	52 0.7×	29 0.9×	13 0.6×	15	549
Sibei Yang China	15	354 0.8×	511 1.4×	91 1.2×	22 0.7×	12 0.6×	26	714
Jiangwei Xie China	6	316 0.7×	219 0.6×	42 0.5×	20 0.6×	20 1.0×	8	426
Saurav Kadavath United States	2	429 0.9×	346 1.0×	48 0.6×	13 0.4×	16 0.8×	2	580
Yemin Shi China	8	301 0.7×	313 0.9×	54 0.7×	26 0.8×	7 0.3×	24	470
Xinyuan Chang China	6	332 0.7×	247 0.7×	53 0.7×	19 0.6×	16 0.8×	8	430

Countries citing papers authored by Shipeng Yan

Since Specialization

Citations

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

Fields of papers citing papers by Shipeng Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shipeng Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Shipeng Yan. A scholar is included among the top collaborators of Shipeng Yan 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 Shipeng Yan. Shipeng Yan 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:

8 of 8 papers shown

1.

Xiao, Haifan, Hao Luo, Xiangyu Liu, et al.. (2024). Comparing Participation and Interim Effectiveness of Endoscopy and Biomarker-Based Screening for Gastric Cancer: A Cluster Randomized Controlled Trial. Journal of Cancer. 15(18). 6110–6121.

2.

Xie, Jiangwei, Shipeng Yan, & Xuming He. (2022). General Incremental Learning with Domain-aware Categorical Representations. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14331–14340. 26 indexed citations

3.

Zhang, Songyang, Zeming Li, Shipeng Yan, Xuming He, & Jian Sun. (2021). Distribution Alignment: A Unified Framework for Long-tail Visual Recognition. 2361–2370. 177 indexed citations

4.

Yan, Shipeng, Jiangwei Xie, & Xuming He. (2021). DER: Dynamically Expandable Representation for Class Incremental Learning. 3013–3022. 312 indexed citations breakdown →

5.

Yan, Shipeng, Jiale Zhou, Jiangwei Xie, Songyang Zhang, & Xuming He. (2021). An EM Framework for Online Incremental Learning of Semantic Segmentation. 3052–3060. 25 indexed citations

6.

Zhang, Songyang, Shipeng Yan, & Xuming He. (2019). LatentGNN: Learning Efficient Non-local Relations for Visual Recognition. International Conference on Machine Learning. 7374–7383. 12 indexed citations

7.

Yan, Shipeng, Songyang Zhang, & Xuming He. (2019). A Dual Attention Network with Semantic Embedding for Few-Shot Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 9079–9086. 12 indexed citations

8.

Huang, Shuaiyi, Qiuyue Wang, Songyang Zhang, Shipeng Yan, & Xuming He. (2019). Dynamic Context Correspondence Network for Semantic Alignment. 2010–2019. 59 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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