Ceyuan Yang

3.8k total citations · 2 hit papers
22 papers, 2.0k citations indexed

About

Ceyuan Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design. According to data from OpenAlex, Ceyuan Yang has authored 22 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 5 papers in Computer Graphics and Computer-Aided Design. Recurrent topics in Ceyuan Yang's work include Generative Adversarial Networks and Image Synthesis (12 papers), Advanced Vision and Imaging (6 papers) and Computer Graphics and Visualization Techniques (5 papers). Ceyuan Yang is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (12 papers), Advanced Vision and Imaging (6 papers) and Computer Graphics and Visualization Techniques (5 papers). Ceyuan Yang collaborates with scholars based in Hong Kong, United States and China. Ceyuan Yang's co-authors include Xiwen Yao, Gong Cheng, Junwei Han, Lei Guo, Bolei Zhou, Jianping Shi, Yinghao Xu, Yujun Shen, Bo Dai and Dahua Lin and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and ACM Transactions on Graphics.

In The Last Decade

Ceyuan Yang

18 papers receiving 1.9k citations

Hit Papers

When Deep Learning Meets Metric Learning: Remote Sensing ... 2018 2026 2020 2023 2018 2020 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ceyuan Yang Hong Kong 12 1.4k 705 661 286 146 22 2.0k
Rao Muhammad Anwer United Arab Emirates 24 1.6k 1.1× 521 0.7× 497 0.8× 193 0.7× 89 0.6× 60 2.1k
Sixiao Zheng China 3 1.6k 1.2× 489 0.7× 707 1.1× 91 0.3× 95 0.7× 5 2.4k
Zhenda Xie China 7 1.4k 1.0× 345 0.5× 786 1.2× 76 0.3× 54 0.4× 10 2.4k
Shuhui Bu China 27 1.6k 1.1× 885 1.3× 203 0.3× 191 0.7× 107 0.7× 90 2.6k
Carlo Gatta Spain 22 1.1k 0.7× 708 1.0× 167 0.3× 229 0.8× 154 1.1× 63 2.0k
Yuhui Zheng China 27 1.6k 1.1× 931 1.3× 435 0.7× 331 1.2× 71 0.5× 96 2.6k
Xiang Bai China 10 1.7k 1.2× 478 0.7× 445 0.7× 55 0.2× 58 0.4× 26 2.3k
Germán Ros Spain 6 1.3k 0.9× 269 0.4× 609 0.9× 125 0.4× 88 0.6× 12 1.7k
Zhuliang Yao China 8 1.6k 1.1× 375 0.5× 752 1.1× 64 0.2× 50 0.3× 9 2.5k

Countries citing papers authored by Ceyuan Yang

Since Specialization
Citations

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

Fields of papers citing papers by Ceyuan Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ceyuan Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Ceyuan Yang. A scholar is included among the top collaborators of Ceyuan 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 Ceyuan Yang. Ceyuan 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.
Shao, Ruizhi, Yinghao Xu, Yujun Shen, et al.. (2025). Interspatial Attention for Efficient 4D Human Video Generation. ACM Transactions on Graphics. 44(4). 1–16.
2.
Zhu, Jiapeng, Ceyuan Yang, Kecheng Zheng, et al.. (2025). Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 18411–18423.
3.
Wang, Jianyi, Wei Meng, Yang Zhao, et al.. (2025). SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration. 2161–2172.
4.
Yang, Ceyuan, Anyi Rao, Chenlin Meng, et al.. (2025). Keyframe-Guided Creative Video Inpainting. 13009–13020. 2 indexed citations
5.
Wang, Jianyuan, Ceyuan Yang, Yinghao Xu, et al.. (2024). Spatial Steerability of GANs via Self-Supervision from Discriminator. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 9493–9507. 2 indexed citations
6.
Zhang, Qihang, Yinghao Xu, Yujun Shen, et al.. (2024). BerfScene: Bev-conditioned Equivariant Radiance Fields for Infinite 3D Scene Generation. 35. 6839–6849. 1 indexed citations
7.
Zhang, Qihang, Chaoyang Wang, Aliaksandr Siarohin, et al.. (2024). Towards Text-guided 3D Scene Composition. 6829–6838. 5 indexed citations
8.
Chen, Xinyuan, Shangchen Zhou, Ziqi Huang, et al.. (2024). LaVie: High-Quality Video Generation with Cascaded Latent Diffusion Models. International Journal of Computer Vision. 133(5). 3059–3078. 25 indexed citations
9.
Bai, Qingyan, Ceyuan Yang, Yinghao Xu, et al.. (2023). GLeaD: Improving GANs with A Generator-Leading Task. The HKU Scholars Hub (University of Hong Kong). 12094–12104. 9 indexed citations
10.
Zhu, Jiapeng, Ceyuan Yang, Yujun Shen, et al.. (2023). LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 7622–7632. 13 indexed citations
11.
Xu, Yinghao, Menglei Chai, Sida Peng, et al.. (2023). DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4402–4412. 25 indexed citations
12.
Xu, Yinghao, Fangyun Wei, Xiao Sun, et al.. (2022). Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 40 indexed citations
13.
Xu, Yinghao, Sida Peng, Ceyuan Yang, Yujun Shen, & Bolei Zhou. (2022). 3D-aware Image Synthesis via Learning Structural and Textural Representations. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 18409–18418. 61 indexed citations
14.
Xu, Yinghao, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, & Bolei Zhou. (2021). Generative Hierarchical Features from Synthesizing Images. 4430–4430. 50 indexed citations
15.
Yang, Ceyuan, Yujun Shen, & Bolei Zhou. (2021). Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis. International Journal of Computer Vision. 129(5). 1451–1466. 94 indexed citations
16.
Yang, Ceyuan, Zhirong Wu, Bolei Zhou, & Stephen Lin. (2021). Instance Localization for Self-supervised Detection Pretraining. 3986–3995. 67 indexed citations
17.
Yang, Ceyuan, Yinghao Xu, Jianping Shi, Bo Dai, & Bolei Zhou. (2020). Temporal Pyramid Network for Action Recognition. The HKU Scholars Hub (University of Hong Kong). 588–597. 279 indexed citations breakdown →
18.
Zhu, Xinge, Jiangmiao Pang, Ceyuan Yang, Jianping Shi, & Dahua Lin. (2019). Adapting Object Detectors via Selective Cross-Domain Alignment. 687–696. 251 indexed citations
19.
Cheng, Gong, Ceyuan Yang, Xiwen Yao, Lei Guo, & Junwei Han. (2018). When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing. 56(5). 2811–2821. 1042 indexed citations breakdown →
20.
Ying, Jun, et al.. (2017). [Severity classification of chronic obstructive pulmonary disease based on deep learning].. PubMed. 34(6). 842–849. 2 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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