Sibei Yang

1.6k total citations · 1 hit paper
26 papers, 714 citations indexed

About

Sibei Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Sibei Yang has authored 26 papers receiving a total of 714 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Sibei Yang's work include Multimodal Machine Learning Applications (14 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Domain Adaptation and Few-Shot Learning (8 papers). Sibei Yang is often cited by papers focused on Multimodal Machine Learning Applications (14 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Domain Adaptation and Few-Shot Learning (8 papers). Sibei Yang collaborates with scholars based in China, Hong Kong and United States. Sibei Yang's co-authors include Yizhou Yu, Guanbin Li, Hong-Yu Zhou, Xiaoguang Han, Chaoqi Chen, Meng Xia, Cheng Shi, Zheng Ge, Jingyi Yu and Zheng Ge and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Sibei Yang

23 papers receiving 710 citations

Hit Papers

TransXNet: Learning Both Global and Local Dynamics With a... 2025 2026 2025 5 10 15
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. TransXNet: Learning Both Global and Local Dynamics With a Dual Dynamic Token Mixer for Visual Recognition
    2025 19 citations Meng Lou, Shu Zhang et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sibei Yang China 15 511 354 91 33 28 26 714
Minghao Xu China 9 483 0.9× 493 1.4× 92 1.0× 19 0.6× 18 0.6× 11 675
Mingjie Sun China 14 347 0.7× 216 0.6× 26 0.3× 41 1.2× 41 1.5× 44 597
Sara Sabour United States 5 269 0.5× 220 0.6× 40 0.4× 16 0.5× 10 0.4× 5 462
Haixian Zhang China 10 206 0.4× 150 0.4× 75 0.8× 42 1.3× 21 0.8× 35 397
E-Liang Chen Taiwan 6 282 0.6× 179 0.5× 96 1.1× 38 1.2× 12 0.4× 10 426
Zhiqiang He China 8 160 0.3× 168 0.5× 59 0.6× 29 0.9× 22 0.8× 25 425
Yemin Shi China 8 313 0.6× 301 0.9× 54 0.6× 59 1.8× 13 0.5× 24 470
Xizhan Gao China 15 253 0.5× 146 0.4× 52 0.6× 21 0.6× 28 1.0× 55 479
Nicholas Frosst United States 4 244 0.5× 234 0.7× 39 0.4× 14 0.4× 10 0.4× 4 434

Countries citing papers authored by Sibei Yang

Since Specialization
Citations

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

Fields of papers citing papers by Sibei Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sibei Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Sibei Yang. A scholar is included among the top collaborators of Sibei Yang 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 Sibei Yang. Sibei Yang 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.
2.
He, Zijian, Ning Yang, Yipeng Qin, et al.. (2025). VTON 360: High-Fidelity Virtual Try-On from Any Viewing Direction. ORCA Online Research @Cardiff (Cardiff University). 26388–26398. 1 indexed citations
3.
4.
Lou, Meng, Shu Zhang, Hong-Yu Zhou, et al.. (2025). TransXNet: Learning Both Global and Local Dynamics With a Dual Dynamic Token Mixer for Visual Recognition. IEEE Transactions on Neural Networks and Learning Systems. 36(6). 11534–11547. 19 indexed citations breakdown →
5.
Chen, Chaoqi, Yushuang Wu, Hong-Yu Zhou, et al.. (2024). A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 10297–10318. 43 indexed citations
6.
Bao, Jiacheng, et al.. (2024). OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers. 482–493. 9 indexed citations
7.
Yang, Sibei, et al.. (2024). Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation. 13711–13722. 3 indexed citations
8.
Liu, Xuyang, et al.. (2023). CCQ: Cross-Class Query Network for Partially Labeled Organ Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence. 37(2). 1755–1763. 4 indexed citations
9.
10.
Wu, Yu, et al.. (2023). Grounded Image Text Matching with Mismatched Relation Reasoning. 2964–2975. 3 indexed citations
11.
Cheng, Shi & Sibei Yang. (2023). LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models. 2920–2929. 7 indexed citations
12.
Ge, Zheng, et al.. (2023). Contrastive Grouping with Transformer for Referring Image Segmentation. 23570–23580. 29 indexed citations
13.
Zhang, Longwen, Qixuan Zhang, Ye Shi, et al.. (2023). DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance. ACM Transactions on Graphics. 42(4). 1–16. 34 indexed citations
14.
Zhou, Hong-Yu, et al.. (2023). A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(7). 1–16. 35 indexed citations
15.
Lin, Liang, et al.. (2021). Structured Attention Network for Referring Image Segmentation. IEEE Transactions on Multimedia. 24. 1922–1932. 31 indexed citations
16.
Yang, Sibei, Meng Xia, Guanbin Li, Hong-Yu Zhou, & Yizhou Yu. (2021). Bottom-Up Shift and Reasoning for Referring Image Segmentation. 11261–11270. 60 indexed citations
17.
Yang, Sibei, Guanbin Li, & Yizhou Yu. (2020). Relationship-Embedded Representation Learning for Grounding Referring Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(8). 2765–2779. 51 indexed citations
18.
Yang, Sibei, Guanbin Li, & Yizhou Yu. (2020). Graph-Structured Referring Expression Reasoning in the Wild. The HKU Scholars Hub (University of Hong Kong). 9949–9958. 62 indexed citations
19.
Yang, Sibei, Guanbin Li, & Yizhou Yu. (2019). Cross-Modal Relationship Inference for Grounding Referring Expressions. 4140–4149. 6 indexed citations
20.
Yang, Sibei, Guanbin Li, & Yizhou Yu. (2019). Dynamic Graph Attention for Referring Expression Comprehension. 4643–4652. 141 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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