Lantao Yu

3.8k total citations · 1 hit paper
27 papers, 1.6k citations indexed

About

Lantao Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Lantao Yu has authored 27 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 3 papers in Media Technology. Recurrent topics in Lantao Yu's work include Image Enhancement Techniques (6 papers), Reinforcement Learning in Robotics (5 papers) and Advanced Image Processing Techniques (5 papers). Lantao Yu is often cited by papers focused on Image Enhancement Techniques (6 papers), Reinforcement Learning in Robotics (5 papers) and Advanced Image Processing Techniques (5 papers). Lantao Yu collaborates with scholars based in United States, China and United Kingdom. Lantao Yu's co-authors include Yong Yu, Weinan Zhang, Jun Wang, Kan Ren, Jun Wang, M.T. Orchard, Yuanhao Gong, Guoping Qiu, Wenming Tang and Yu-Fei Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Instrumentation and Measurement and IEEE Robotics and Automation Letters.

In The Last Decade

Lantao Yu

22 papers receiving 1.5k 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.

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

2017 1.3k citations Lantao Yu, Weinan Zhang et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lantao Yu United States	9	930	664	230	219	96	27	1.6k
Ayhan Demiriz Türkiye	11	888 1.0×	550 0.8×	168 0.7×	148 0.7×	114 1.2×	30	1.4k
Jilian Zhang China	17	794 0.9×	333 0.5×	268 1.2×	114 0.5×	110 1.1×	42	1.2k
Lifeng Shang China	20	1.5k 1.6×	896 1.3×	257 1.1×	117 0.5×	72 0.8×	83	2.1k
Ulf Brefeld Germany	20	891 1.0×	381 0.6×	151 0.7×	187 0.9×	232 2.4×	64	1.4k
Bo Long United States	14	756 0.8×	270 0.4×	309 1.3×	99 0.5×	143 1.5×	32	1.2k
Tie‐Yan Liu China	21	1.0k 1.1×	450 0.7×	122 0.5×	173 0.8×	41 0.4×	62	1.4k
Octavian Udrea United States	16	1.0k 1.1×	1.0k 1.6×	222 1.0×	245 1.1×	413 4.3×	37	1.8k
Anne M. P. Canuto Brazil	16	619 0.7×	254 0.4×	192 0.8×	141 0.6×	44 0.5×	135	935
Xiaoyong Liu China	17	773 0.8×	335 0.5×	557 2.4×	99 0.5×	78 0.8×	60	1.4k

Countries citing papers authored by Lantao Yu

Since Specialization

Citations

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

Fields of papers citing papers by Lantao Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lantao Yu

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

20 of 20 papers shown

1.

Zhang, Shuai, et al.. (2025). Ultra-Malin: Robotic Ultrasound Mapping and Localization via Implicit Neural Representation. IEEE Transactions on Instrumentation and Measurement. 74. 1–12. 2 indexed citations

2.

Liu, Zichuan, et al.. (2024). I-Matting: Improved Trimap-Free Image Matting. 1–6.

3.

Tu, Zhi, Wentian Zhao, Lantao Yu, et al.. (2024). DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision. 22160–22169. 28 indexed citations

4.

Yu, Lantao, Tianhe Yu, Jiaming Song, Willie Neiswanger, & Stefano Ermon. (2023). Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 11016–11024. 3 indexed citations

5.

Gong, Yuanhao, et al.. (2021). A Discrete Scheme for Computing Image’s Weighted Gaussian Curvature. 1919–1923. 13 indexed citations

6.

Gong, Yuanhao, et al.. (2021). Quarter Laplacian Filter For Edge Aware Image Processing. 1959–1963. 12 indexed citations

7.

Li, Yi, Chuang Liu, Lantao Yu, et al.. (2021). Mechanical Principle of Piezoelectric-Promoted Mechanical Antenna. 955–956. 2 indexed citations

8.

Yu, Tianhe, Garrett Thomas, Lantao Yu, et al.. (2020). MOPO: Model-based Offline Policy Optimization. arXiv (Cornell University). 33. 14129–14142. 7 indexed citations

9.

Miao, Ning, et al.. (2020). Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation. arXiv (Cornell University). 122–132. 2 indexed citations

10.

Yu, Lantao, Dehong Liu, Hassan Mansour, Petros T. Boufounos, & Yanting Ma. (2020). Blind Multi-Spectral Image Pan-Sharpening. 6 indexed citations

11.

Xu, Minkai, et al.. (2020). Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5834–5841. 13 indexed citations

12.

Zhou, Zhiming, Yuxuan Song, Lantao Yu, et al.. (2019). Lipschitz Generative Adversarial Nets.. arXiv (Cornell University). 7584–7593. 5 indexed citations

13.

Yu, Lantao & M.T. Orchard. (2019). Single Image Interpolation Exploiting Semi-local Similarity. 1722–1726. 11 indexed citations

14.

Yu, Lantao & M.T. Orchard. (2019). When Spatially-Variant Filtering Meets Low-Rank Regularization: Exploiting Non-Local Similarity for Single Image Interpolation. 2. 200–204.

15.

Zhou, Zhiming, et al.. (2018). Understanding the Effectiveness of Lipschitz Constraint in Training of GANs via Gradient Analysis. arXiv (Cornell University). 2 indexed citations

16.

Lu, Sidi, Lantao Yu, Siyuan Feng, et al.. (2018). CoT: Cooperative Training for Generative Modeling of Discrete Data. International Conference on Machine Learning. 4164–4172. 6 indexed citations

17.

Lu, Sidi, Lantao Yu, Weinan Zhang, & Yong Yu. (2018). CoT: Cooperative Training for Generative Modeling.. arXiv (Cornell University). 5 indexed citations

18.

Yu, Lantao & M.T. Orchard. (2018). Location-Directed Image Modeling and its Application to Image Interpolation. 2192–2196. 8 indexed citations

19.

Yang, Yaodong, Lantao Yu, Yiwei Bai, et al.. (2017). An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning.. 2 indexed citations

20.

Yu, Lantao, Weinan Zhang, Jun Wang, & Yong Yu. (2017). SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 1277 indexed citations breakdown →

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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