Shiliang Pu
- Computer Vision and Pattern Recognition top 0.2%
- Artificial Intelligence top 0.5%
- Biomedical Engineering top 10%
- Media Technology top 1%
- Signal Processing top 2%
- Topics
- Domain Adaptation and Few-Shot Learning (34 papers)Multimodal Machine Learning Applications (26 papers)Advanced Neural Network Applications (22 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image ProcessingIEEE Access
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Shiliang Pu
144 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Computer Vision and Pattern Recognition 2.9k
- Artificial Intelligence 1.8k
- Biomedical Engineering 385
- Media Technology 358
- Signal Processing 298
Countries citing papers authored by Shiliang Pu
This map shows the geographic impact of Shiliang Pu'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 Shiliang Pu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shiliang Pu more than expected).
Fields of papers citing papers by Shiliang Pu
This network shows the impact of papers produced by Shiliang Pu. 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 Shiliang Pu. The network helps show where Shiliang Pu may publish in the future.
Co-authorship network of co-authors of Shiliang Pu
This figure shows the co-authorship network connecting the top 25 collaborators of Shiliang Pu. A scholar is included among the top collaborators of Shiliang Pu 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 Shiliang Pu. Shiliang Pu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 16 | |
| 3 | 4 | |
| 4 | 3 | |
| 5 | 5 | |
| 6 | 7 | |
| 7 | 10 | |
| 8 | 36 | |
| 9 | 2 | |
| 10 | 27 | |
| 11 | 31 | |
| 12 | 2 | |
| 13 | STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data | 8 |
| 14 | 21 | |
| 15 | 6 | |
| 16 | AutoETER: Automated Entity Type Representation with Relation-Aware Attention for Knowledge Graph Embedding. | 2 |
| 17 | 144 | |
| 18 | Scene Dynamics: Counterfactual Critic Multi-Agent Training for Scene Graph Generation. | 7 |
| 19 | No-Reference Image Blur Assessment Based on SIFT and DCT. | 3 |
| 20 | Arbitrarily-Oriented Text Recognition. | 6 |
About Shiliang Pu
Shiliang Pu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Geology, having authored 150 papers that have together received 3.9k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (34 papers), Multimodal Machine Learning Applications (26 papers) and Advanced Neural Network Applications (22 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.9k citations), Artificial Intelligence (1.8k citations) and Media Technology (358 citations). Shiliang Pu has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Di Xie, Qiaoyong Zhong, Zhanzhan Cheng, Chao Li, Yueting Zhuang, Yi Niu, Jun Xiao, Fei Wu, Shuigeng Zhou and Long Chen. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Access.
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.