Thomas H. Li

2.5k total citations · 1 hit paper
35 papers, 1.3k citations indexed

About

Thomas H. Li is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Aerospace Engineering. According to data from OpenAlex, Thomas H. Li has authored 35 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 8 papers in Computational Mechanics and 7 papers in Aerospace Engineering. Recurrent topics in Thomas H. Li's work include Advanced Vision and Imaging (15 papers), Generative Adversarial Networks and Image Synthesis (8 papers) and 3D Shape Modeling and Analysis (8 papers). Thomas H. Li is often cited by papers focused on Advanced Vision and Imaging (15 papers), Generative Adversarial Networks and Image Synthesis (8 papers) and 3D Shape Modeling and Analysis (8 papers). Thomas H. Li collaborates with scholars based in China and United States. Thomas H. Li's co-authors include Ge Li, Shan Liu, Yurui Ren, Weijie Kong, Jia-Xing Zhong, Nannan Li, Ruonan Zhang, Xiaoming Yu, Zhenqiang Ying and Yuanqi Chen and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Thomas H. Li

32 papers receiving 1.2k citations

Hit Papers

Graph Convolutional Label Noise Cleaner: Train a Plug-And... 2019 2026 2021 2023 2019 100 200 300
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. Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection
    2019 376 citations Jia-Xing Zhong, Nannan Li et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas H. Li China 15 957 395 230 149 126 35 1.3k
Yongwei Nie China 17 805 0.8× 269 0.7× 156 0.7× 79 0.5× 105 0.8× 62 1.0k
Yigang Cen China 18 734 0.8× 255 0.6× 70 0.3× 178 1.2× 89 0.7× 114 1.1k
Anoop Cherian United States 21 836 0.9× 425 1.1× 73 0.3× 75 0.5× 141 1.1× 68 1.2k
Yanghai Tsin United States 13 1.4k 1.4× 183 0.5× 47 0.2× 150 1.0× 70 0.6× 26 1.5k
Anhong Wang China 19 749 0.8× 99 0.3× 91 0.4× 182 1.2× 111 0.9× 170 1.2k
Mahsa Baktashmotlagh Australia 15 482 0.5× 580 1.5× 67 0.3× 45 0.3× 45 0.4× 39 945
Simone Milani Italy 19 1.2k 1.2× 101 0.3× 76 0.3× 116 0.8× 37 0.3× 112 1.4k
Chenglin Li China 20 632 0.7× 165 0.4× 358 1.6× 64 0.4× 45 0.4× 121 1.1k
M.G. Strintzis Greece 16 573 0.6× 197 0.5× 85 0.4× 43 0.3× 131 1.0× 72 1.1k
Kim–Hui Yap Singapore 22 1.3k 1.3× 186 0.5× 36 0.2× 329 2.2× 174 1.4× 138 1.6k

Countries citing papers authored by Thomas H. Li

Since Specialization
Citations

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

Fields of papers citing papers by Thomas H. Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas H. Li. 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 Thomas H. Li. The network helps show where Thomas H. Li may publish in the future.

Co-authorship network of co-authors of Thomas H. Li

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas H. Li. A scholar is included among the top collaborators of Thomas H. Li 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 Thomas H. Li. Thomas H. Li 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.
Li, Chen, et al.. (2024). BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning. 13658–13667. 3 indexed citations
2.
Li, Ge, et al.. (2024). ComPoint: Can Complex-Valued Representation Benefit Point Cloud Place Recognition?. IEEE Transactions on Intelligent Transportation Systems. 25(7). 7494–7507. 5 indexed citations
3.
Chen, Yuanqi, et al.. (2023). Mitigating Label Noise in GANs via Enhanced Spectral Normalization. IEEE Transactions on Circuits and Systems for Video Technology. 33(8). 3924–3934. 8 indexed citations
4.
5.
Zhang, Nan, et al.. (2023). Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering. 1244–1254. 23 indexed citations
6.
Song, Fei, Ge Li, Xiaodong Yang, Wei Gao, & Thomas H. Li. (2022). Fine-Grained Correlation Representation for Graph-Based Point Cloud Attribute Compression. 2022 IEEE International Conference on Multimedia and Expo (ICME). 1–6. 6 indexed citations
7.
Li, Zhuangzi, Ge Li, Thomas H. Li, Shan Liu, & Wei Gao. (2022). Semantic Point Cloud Upsampling. IEEE Transactions on Multimedia. 25. 3432–3442. 27 indexed citations
8.
Li, Jiapeng, Ge Li, & Thomas H. Li. (2022). Attention Guided Invariance Selection for Local Feature Descriptors. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2215–2219. 4 indexed citations
9.
Liu, Li, et al.. (2022). DKNAS: A Practical Deep Keypoint Extraction Framework Based on Neural Architecture Search. 2022 International Conference on Robotics and Automation (ICRA). 28. 5643–5649. 1 indexed citations
10.
Zhang, Ruonan, et al.. (2022). PointOT: Interpretable Geometry-Inspired Point Cloud Generative Model via Optimal Transport. IEEE Transactions on Circuits and Systems for Video Technology. 32(10). 6792–6806. 15 indexed citations
11.
Ren, Yurui, et al.. (2022). Neural Texture Extraction and Distribution for Controllable Person Image Synthesis. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 13525–13534. 42 indexed citations
12.
Gao, Wei, et al.. (2022). QINet: Decision Surface Learning and Adversarial Enhancement for Quasi-Immune Completion of Diverse Corrupted Point Clouds. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–14. 10 indexed citations
13.
Li, Chengyuan, Ge Li, & Thomas H. Li. (2021). Separating Left and Right Images in Unsupervised Monocular Depth Estimation. 65–69.
14.
Liu, Li, Ge Li, & Thomas H. Li. (2021). ATVIO: Attention Guided Visual-Inertial Odometry. 4125–4129. 14 indexed citations
15.
16.
Zhong, Jia-Xing, Nannan Li, Weijie Kong, et al.. (2019). Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection. 1237–1246. 376 indexed citations breakdown →
17.
Yu, Xiaoming, Yuanqi Chen, Thomas H. Li, Shan Liu, & Ge Li. (2019). Multi-mapping Image-to-Image Translation via Learning Disentanglement. arXiv (Cornell University). 32. 2990–2999. 16 indexed citations
18.
Li, Thomas H., et al.. (2019). Real Photographs Denoising With Noise Domain Adaptation and Attentive Generative Adversarial Network. 1717–1721. 30 indexed citations
19.
Ren, Yurui, Zhenqiang Ying, Thomas H. Li, & Ge Li. (2018). LECARM: Low-Light Image Enhancement Using the Camera Response Model. IEEE Transactions on Circuits and Systems for Video Technology. 29(4). 968–981. 171 indexed citations
20.

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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