Lihe Yang
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Medical Image Segmentation Techniques
- Advanced Vision and Imaging
- Media Technology top 2%
- Remote-Sensing Image Classification
Papers in
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- Advanced Neural Network Applications 6
- Advanced Image and Video Retrieval Techniques 1
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- Domain Adaptation and Few-Shot Learning 5
- Machine Learning and Data Classification 2
- Co-authors
- Yinghuan Shi (3 shared papers)Lei Qi (3 shared papers)Wei Zhuo (1 shared paper)Yang Gao (1 shared paper)Hengshuang Zhao (3 shared papers)Jiashi Feng (2 shared papers)Xiaogang Xu (2 shared papers)Bingyi Kang (2 shared papers)
- Journals
- Transactions of Tianjin University (1 paper)IEEE Signal Processing Letters (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
In The Last Decade
Lihe Yang
9 papers receiving 920 citations
Lihe Yang's Hit Papers
Peers
Comparison fields: 5 of 91
- Computer Vision and Pattern Recognition 570
- Media Technology 169
- Artificial Intelligence 281
- Computer Graphics and Computer-Aided Design 23
- Neurology 55
Countries citing papers authored by Lihe Yang
This map shows the geographic impact of Lihe 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 Lihe Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lihe Yang more than expected).
Fields of papers citing papers by Lihe Yang
This network shows the impact of papers produced by Lihe 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 Lihe Yang. The network helps show where Lihe Yang may publish in the future.
Co-authors
The 23 scholars most cited alongside Lihe Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data Hit paper breakdown → | 2024 | 292 |
| 2 | ST++: Make Self-trainingWork Better for Semi-supervised Semantic Segmentation Hit paper breakdown → | 2022 | 286 |
| 3 | Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation Hit paper breakdown → | 2023 | 235 |
| 4 | 2023 | 62 | |
| 5 | 2023 | 20 | |
| 6 | 2024 | 18 | |
| 7 | 2023 | 8 | |
| 8 | 2023 | 6 | |
| 9 | 2008 | 4 | |
| 10 | 2023 | 0 |
About Lihe Yang
Lihe Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Civil and Structural Engineering, Industrial and Manufacturing Engineering and Computational Mechanics, having authored 10 papers that have together received 931 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (6 papers), Domain Adaptation and Few-Shot Learning (5 papers), Machine Learning and Data Classification (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Optical Systems and Laser Technology (1 paper), Infrastructure Maintenance and Monitoring (1 paper), Analytical chemistry methods development (1 paper) and Water Quality Monitoring and Analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (570 citations), Media Technology (169 citations), Artificial Intelligence (281 citations), Computer Graphics and Computer-Aided Design (23 citations) and Neurology (55 citations). Lihe Yang has collaborated with scholars based in China, Hong Kong and Australia. Frequent co-authors include Yinghuan Shi, Lei Qi, Wei Zhuo, Yang Gao, Hengshuang Zhao, Jiashi Feng, Xiaogang Xu, Bingyi Kang, Wei Zhang and Litong Feng. Their work appears in journals such as Transactions of Tianjin University, IEEE Signal Processing Letters and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.