Ping Luo
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Face recognition and analysis
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Advanced Image Processing Techniques
- Face and Expression Recognition
- Media Technology top 0.1%
Papers in
-
- Advanced Neural Network Applications 64
- Advanced Image and Video Retrieval Techniques 36
- Generative Adversarial Networks and Image Synthesis 30
- Face recognition and analysis 25
- Multimodal Machine Learning Applications 23
- Video Surveillance and Tracking Methods 20
- Human Pose and Action Recognition 19
-
- Domain Adaptation and Few-Shot Learning 43
- Co-authors
- Xiaoou Tang (27 shared papers)Xiaogang Wang (34 shared papers)Ziwei Liu (10 shared papers)Enze Xie (16 shared papers)Wenhai Wang (9 shared papers)Ding Liang (6 shared papers)Tong Lü (4 shared papers)Kaitao Song (2 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (14 papers)International Journal of Computer Vision (5 papers)IEEE Transactions on Image Processing (4 papers)Vaccine (4 papers)Cell Death and Disease (3 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Ping Luo
226 papers receiving 21.9k citations
Ping Luo's Hit Papers
Peers
Comparison fields: 5 of 212
- Computer Vision and Pattern Recognition 16.2k
- Media Technology 1.9k
- Artificial Intelligence 5.1k
- Computer Graphics and Computer-Aided Design 540
- Signal Processing 1.5k
Countries citing papers authored by Ping Luo
This map shows the geographic impact of Ping Luo'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 Ping Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ping Luo more than expected).
Fields of papers citing papers by Ping Luo
This network shows the impact of papers produced by Ping Luo. 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 Ping Luo. The network helps show where Ping Luo may publish in the future.
Co-authors
The 25 scholars most cited alongside Ping Luo, 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 239 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Learning Face Attributes in the Wild Hit paper breakdown → | 2015 | 3964 |
| 2 | Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Hit paper breakdown → | 2021 | 3156 |
| 3 | PVT v2: Improved baselines with pyramid vision transformer Hit paper breakdown → | 2022 | 1359 |
| 4 | DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations Hit paper breakdown → | 2016 | 1053 |
| 5 | Sparse R-CNN: End-to-End Object Detection with Learnable Proposals Hit paper breakdown → | 2021 | 970 |
| 6 | Spatial as Deep: Spatial CNN for Traffic Scene Understanding Hit paper breakdown → | 2018 | 710 |
| 7 | MaskGAN: Towards Diverse and Interactive Facial Image Manipulation Hit paper breakdown → | 2020 | 615 |
| 8 | PolarMask: Single Shot Instance Segmentation With Polar Representation Hit paper breakdown → | 2020 | 443 |
| 9 | 2017 | 398 | |
| 10 | Deep Learning Strong Parts for Pedestrian Detection Hit paper breakdown → | 2015 | 378 |
| 11 | From Facial Parts Responses to Face Detection: A Deep Learning Approach Hit paper breakdown → | 2015 | 320 |
| 12 | DiffusionDet: Diffusion Model for Object Detection Hit paper breakdown → | 2023 | 297 |
| 13 | 2014 | 283 | |
| 14 | Learning Deep Representation for Face Alignment with Auxiliary Attributes Hit paper breakdown → | 2015 | 272 |
| 15 | Talking Face Generation by Adversarially Disentangled Audio-Visual Representation Hit paper breakdown → | 2019 | 259 |
| 16 | 2013 | 233 | |
| 17 | DetCo: Unsupervised Contrastive Learning for Object Detection Hit paper breakdown → | 2021 | 214 |
| 18 | 2017 | 203 | |
| 19 | 2020 | 200 | |
| 20 | DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion Hit paper breakdown → | 2022 | 196 |
About Ping Luo
Ping Luo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Immunology and Infectious Diseases, having authored 239 papers that have together received 22.5k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (64 papers), Domain Adaptation and Few-Shot Learning (43 papers), Advanced Image and Video Retrieval Techniques (36 papers), Generative Adversarial Networks and Image Synthesis (30 papers), Face recognition and analysis (25 papers), Multimodal Machine Learning Applications (23 papers), Video Surveillance and Tracking Methods (20 papers) and Human Pose and Action Recognition (19 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (16.2k citations), Media Technology (1.9k citations), Artificial Intelligence (5.1k citations), Computer Graphics and Computer-Aided Design (540 citations) and Signal Processing (1.5k citations). Ping Luo has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Xiaoou Tang, Xiaogang Wang, Ziwei Liu, Enze Xie, Wenhai Wang, Ding Liang, Tong Lü, Kaitao Song, Deng-Ping Fan and Xiang Li. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, IEEE Transactions on Image Processing, Vaccine and Cell Death and Disease.
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.