Kaicheng Yu

1.2k total citations · 1 hit paper
18 papers, 334 citations indexed

About

Kaicheng Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Kaicheng Yu has authored 18 papers receiving a total of 334 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 3 papers in Aerospace Engineering. Recurrent topics in Kaicheng Yu's work include Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Robotics and Sensor-Based Localization (3 papers). Kaicheng Yu is often cited by papers focused on Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Robotics and Sensor-Based Localization (3 papers). Kaicheng Yu collaborates with scholars based in China, Switzerland and United States. Kaicheng Yu's co-authors include Mathieu Salzmann, Pascal Fua, Wei Wang, Claudiu Musat, Martin Jaggi, Jian Zhao, Hao Tian, Xiao Xue, Walt A. de Heer and Kaimin Zhang and has published in prestigious journals such as Nature, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Materials Chemistry A.

In The Last Decade

Kaicheng Yu

18 papers receiving 321 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.

Ultrahigh-mobility semiconducting epitaxial graphene on silicon carbide

2024 89 citations Jian Zhao, Yaqi Li et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kaicheng Yu China	8	161	112	81	49	46	18	334
Suhyeon Lee South Korea	11	241 1.5×	110 1.0×	85 1.0×	22 0.4×	65 1.4×	36	419
Y.S. Zhang China	8	129 0.8×	85 0.8×	140 1.7×	27 0.6×	39 0.8×	18	475
Pengyu Zhang China	9	283 1.8×	38 0.3×	82 1.0×	43 0.9×	82 1.8×	37	466
Ge Song China	13	215 1.3×	82 0.7×	54 0.7×	48 1.0×	18 0.4×	36	452
Shixiang Tang China	14	424 2.6×	218 1.9×	69 0.9×	147 3.0×	21 0.5×	26	639
Nicholas J. Weston United Kingdom	8	197 1.2×	104 0.9×	23 0.3×	50 1.0×	20 0.4×	11	412
Linlin Yang China	13	354 2.2×	73 0.7×	48 0.6×	123 2.5×	151 3.3×	31	580
Wenqi Wang China	8	76 0.5×	46 0.4×	15 0.2×	38 0.8×	23 0.5×	30	291
Fereshteh Falah Chamasemani Austria	9	78 0.5×	37 0.3×	28 0.3×	27 0.6×	78 1.7×	22	268

Countries citing papers authored by Kaicheng Yu

Since Specialization

Citations

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

Fields of papers citing papers by Kaicheng Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaicheng Yu

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

All Works

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18 of 18 papers shown

Yang, Lei, Jun Li, Kun Yuan, et al.. (2025). BEVHeight++: Toward Robust Visual Centric 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(6). 5094–5111. 3 indexed citations

Wang, Wanting, Wei Hua, Hong Meng, et al.. (2025). An in-depth understanding of highly crystalline carbon nitride for near-infrared photocatalysis: from n → π* transition to midgap state engineering. Journal of Materials Chemistry A. 13(29). 23312–23322. 1 indexed citations

Wang, Guangrun, Yixing Lao, Peng Chen, et al.. (2024). LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields. 390–398. 10 indexed citations

Yu, Kaicheng, et al.. (2024). Tutorial: Large Language-Vision Model in Society. 11298–11299. 1 indexed citations

He, Shibo, et al.. (2024). Semantic-DARTS: Elevating Semantic Learning for Mobile Differentiable Architecture Search. IEEE Internet of Things Journal. 12(2). 1673–1687. 1 indexed citations

Wu, Xiaoyang, Zhuotao Tian, Xin Wen, et al.. (2024). Towards Large-Scale 3D Representation Learning with Multi-Dataset Point Prompt Training. 19551–19562. 24 indexed citations

Zhao, Jian, Yaqi Li, Kaimin Zhang, et al.. (2024). Ultrahigh-mobility semiconducting epitaxial graphene on silicon carbide. Nature. 625(7993). 60–65. 89 indexed citations breakdown →

Yu, Kaicheng, Xinzhong Zhang, Peng Zheng, et al.. (2023). Relaxor regulation and enhancement of energy storage properties in bi-modified Sr2NaNb5O15-based tetragonal tungsten bronze ceramics. Journal of Materials Science Materials in Electronics. 34(31). 6 indexed citations

Yu, Kaicheng, Hao Tian, Rui Li, et al.. (2023). Electron-Beam Direct Writing-Based High-Performance Graphene Electrode Fabrication. ACS Applied Electronic Materials. 5(9). 5187–5192. 1 indexed citations

10.

Zhao, Jian, et al.. (2022). Research Progress of Graphene Based Photodetectors Under Different Response Mechanisms. Chinese Journal of Luminescence. 43(4). 552–575. 3 indexed citations

11.

Yu, Kaicheng, René Ranftl, & Mathieu Salzmann. (2021). Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 13718–13727. 10 indexed citations

12.

Yu, Kaicheng, René Ranftl, & Mathieu Salzmann. (2021). An Analysis of Super-Net Heuristics in Weight-Sharing NAS. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(11). 1–1. 4 indexed citations

13.

Hu, Xiaobin, Wenqi Ren, Kaicheng Yu, et al.. (2021). Pyramid Architecture Search for Real-Time Image Deblurring. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 4278–4287. 23 indexed citations

14.

Yu, Kaicheng, et al.. (2020). Evaluating The Search Phase of Neural Architecture Search. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 55 indexed citations

15.

Mi, Fei, Lingjing Kong, Tao Lin, Kaicheng Yu, & Boi Faltings. (2020). Generalized Class Incremental Learning. 970–974. 22 indexed citations

16.

Yu, Kaicheng, et al.. (2019). Overcoming Multi-Model Forgetting. ArXiv.org. 97. 594–603. 5 indexed citations

17.

Wang, Wei, et al.. (2019). Recurrent U-Net for Resource-Constrained Segmentation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2142–2151. 75 indexed citations

18.

Wolfe, Jeremy M., et al.. (1988). A pre-attentive feature process can execute only one command at a time. 1 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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