Panqu Wang
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- Face recognition and analysis 3
- Visual Attention and Saliency Detection 3
- Multimodal Machine Learning Applications 1
- Advanced Neural Network Applications 1
- Media Technology top 1%
- Artificial Intelligence top 5%
- Neural Networks and Applications 1
- Domain Adaptation and Few-Shot Learning 1
- Ocean Engineering top 5%
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- Face Recognition and Perception 3
- Neural dynamics and brain function 1
- Co-authors
- Garrison W. CottrellZehua HuangDing LiuYe YuanPengfei ChenXiaodi HouL. GauthierChristopher Kanan
- Journals
- Cognitive Science (2 papers)Journal of Vision (1 paper)Journal of Cognitive Neuroscience (1 paper)
- Partner nations
- United States
In The Last Decade
Panqu Wang
6 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Computer Vision and Pattern Recognition 895
- Media Technology 280
- Artificial Intelligence 299
- Industrial and Manufacturing Engineering 83
- Ocean Engineering 103
Countries citing papers authored by Panqu Wang
This map shows the geographic impact of Panqu Wang'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 Panqu Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Panqu Wang more than expected).
Fields of papers citing papers by Panqu Wang
This network shows the impact of papers produced by Panqu Wang. 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 Panqu Wang. The network helps show where Panqu Wang may publish in the future.
Co-authorship network
The 8 scholars most cited alongside Panqu Wang, 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 | Understanding Convolution for Semantic Segmentationbreakdown → | 2018 | 1366 |
| 2 | 2016 | 13 | |
| 3 | Modeling the Object Recognition Pathway: A Deep Hierarchical Model Using Gnostic Fields. | 2015 | 2 |
| 4 | 2015 | 1 | |
| 5 | Experience Matters: Modeling the Relationship Between Face and Object Recognition | 2014 | 2 |
| 6 | A Computational Model of the Development of Hemispheric Asymmetry of Face Processing | 2013 | 5 |
About Panqu Wang
Panqu Wang is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence and Infectious Diseases, having authored 6 papers that have together received 1.4k indexed citations. Recurring topics across this work include Face Recognition and Perception (3 papers), Face recognition and analysis (3 papers), Visual Attention and Saliency Detection (3 papers), Neural dynamics and brain function (1 paper), Multimodal Machine Learning Applications (1 paper), Neural Networks and Applications (1 paper), Advanced Neural Network Applications (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (895 citations), Media Technology (280 citations), Artificial Intelligence (299 citations), Industrial and Manufacturing Engineering (83 citations) and Ocean Engineering (103 citations). Panqu Wang has collaborated with scholars based in United States. Frequent co-authors include Garrison W. Cottrell, Zehua Huang, Ding Liu, Ye Yuan, Pengfei Chen, Xiaodi Hou, L. Gauthier and Christopher Kanan. Their work appears in journals such as Cognitive Science, Journal of Vision, Journal of Cognitive Neuroscience and eScholarship (California Digital Library).
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.