Wei Liu
Impact in
- Computational Mathematics top 0.1%
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Multimodal Machine Learning Applications
- Image Retrieval and Classification Techniques
- Face and Expression Recognition
- Advanced Neural Network Applications
- Face recognition and analysis
Papers in
-
- Advanced Image and Video Retrieval Techniques 150
- Video Surveillance and Tracking Methods 78
- Face and Expression Recognition 62
- Advanced Vision and Imaging 60
- Image Retrieval and Classification Techniques 59
- Multimodal Machine Learning Applications 57
- Advanced Neural Network Applications 48
-
- Domain Adaptation and Few-Shot Learning 70
- Co-authors
- Shih‐Fu Chang (16 shared papers)Hanwang Zhang (8 shared papers)Tat‐Seng Chua (3 shared papers)Liqiang Nie (5 shared papers)Dacheng Tao (23 shared papers)Lin Ma (17 shared papers)Dihong Gong (6 shared papers)Rongrong Ji (19 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (24 papers)IEEE Transactions on Image Processing (16 papers)Neurocomputing (10 papers)IEEE Transactions on Circuits and Systems for Video Technology (9 papers)Applied Sciences (8 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Wei Liu
763 papers receiving 27.0k citations
Wei Liu's Hit Papers
Peers
Comparison fields: 5 of 217
- Computational Mathematics 613
- Computer Vision and Pattern Recognition 18.7k
- Media Technology 2.9k
- Artificial Intelligence 7.6k
- Signal Processing 2.2k
Countries citing papers authored by Wei Liu
This map shows the geographic impact of Wei Liu'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 Wei Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Liu more than expected).
Fields of papers citing papers by Wei Liu
This network shows the impact of papers produced by Wei Liu. 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 Wei Liu. The network helps show where Wei Liu may publish in the future.
Co-authors
The 25 scholars most cited alongside Wei Liu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 829 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | CosFace: Large Margin Cosine Loss for Deep Face Recognition Hit paper breakdown → | 2018 | 1718 |
| 2 | SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning Hit paper breakdown → | 2017 | 1429 |
| 3 | Supervised hashing with kernels Hit paper breakdown → | 2012 | 1029 |
| 4 | Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation Hit paper breakdown → | 2021 | 959 |
| 5 | Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm Hit paper breakdown → | 2019 | 754 |
| 6 | Beyond Brightening Low-light Images Hit paper breakdown → | 2021 | 588 |
| 7 | Attentive Collaborative Filtering Hit paper breakdown → | 2017 | 580 |
| 8 | Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization Hit paper breakdown → | 2016 | 397 |
| 9 | Large Graph Construction for Scalable Semi-Supervised Learning Hit paper breakdown → | 2010 | 351 |
| 10 | Learning to Hash for Indexing Big Data—A Survey Hit paper breakdown → | 2015 | 346 |
| 11 | Learning to Compose Dynamic Tree Structures for Visual Contexts Hit paper breakdown → | 2019 | 342 |
| 12 | Discrete Graph Hashing Hit paper breakdown → | 2014 | 332 |
| 13 | Generalizing Face Forgery Detection with High-frequency Features Hit paper breakdown → | 2021 | 304 |
| 14 | Unsupervised Deep Tracking Hit paper breakdown → | 2019 | 292 |
| 15 | Deblurring by Realistic Blurring Hit paper breakdown → | 2020 | 285 |
| 16 | A Sufficient Condition for Convergences of Adam and RMSProp Hit paper breakdown → | 2019 | 271 |
| 17 | 2017 | 259 | |
| 18 | 2019 | 259 | |
| 19 | 2011 | 253 | |
| 20 | 2020 | 240 |
About Wei Liu
Wei Liu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Media Technology and Computational Mechanics, having authored 829 papers that have together received 27.7k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (150 papers), Video Surveillance and Tracking Methods (78 papers), Domain Adaptation and Few-Shot Learning (70 papers), Face and Expression Recognition (62 papers), Advanced Vision and Imaging (60 papers), Image Retrieval and Classification Techniques (59 papers), Multimodal Machine Learning Applications (57 papers) and Advanced Neural Network Applications (48 papers). The work is most often cited by research in Computational Mathematics (613 citations), Computer Vision and Pattern Recognition (18.7k citations), Media Technology (2.9k citations), Artificial Intelligence (7.6k citations) and Signal Processing (2.2k citations). Wei Liu has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Shih‐Fu Chang, Hanwang Zhang, Tat‐Seng Chua, Liqiang Nie, Dacheng Tao, Lin Ma, Dihong Gong, Rongrong Ji, Yu–Gang Jiang and Wenhan Luo. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, Neurocomputing, IEEE Transactions on Circuits and Systems for Video Technology and Applied Sciences.
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.