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
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Automotive Engineering top 0.2%
- Autonomous Vehicle Technology and Safety
Papers in
-
- Advanced Neural Network Applications 94
- Generative Adversarial Networks and Image Synthesis 22
- Multimodal Machine Learning Applications 21
- Human Pose and Action Recognition 20
- Face recognition and analysis 17
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- Domain Adaptation and Few-Shot Learning 19
- Co-authors
- Xiaoou Tang (34 shared papers)Xiaogang Wang (36 shared papers)Ziwei Liu (12 shared papers)Chen Change Loy (20 shared papers)Enze Xie (21 shared papers)Wenhai Wang (12 shared papers)Ding Liang (7 shared papers)Tong Lü (6 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)Lecture notes in computer science (29 papers)IEEE Transactions on Neural Networks and Learning Systems (3 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Ping Luo
242 papers receiving 34.9k citations
Ping Luo's Hit Papers
Peers
Comparison fields: 5 of 174
- Computer Vision and Pattern Recognition 26.1k
- Automotive Engineering 1.5k
- Artificial Intelligence 5k
- Media Technology 1.2k
- Signal Processing 584
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 260 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 | 5018 |
| 2 | Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Hit paper breakdown → | 2021 | 3739 |
| 3 | PVT v2: Improved baselines with pyramid vision transformer Hit paper breakdown → | 2022 | 1706 |
| 4 | ByteTrack: Multi-object Tracking by Associating Every Detection Box Hit paper breakdown → | 2022 | 1453 |
| 5 | WIDER FACE: A Face Detection Benchmark Hit paper breakdown → | 2016 | 1347 |
| 6 | DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations Hit paper breakdown → | 2016 | 1320 |
| 7 | Sparse R-CNN: End-to-End Object Detection with Learnable Proposals Hit paper breakdown → | 2021 | 1159 |
| 8 | Facial Landmark Detection by Deep Multi-task Learning Hit paper breakdown → | 2014 | 1030 |
| 9 | Spatial as Deep: Spatial CNN for Traffic Scene Understanding Hit paper breakdown → | 2018 | 860 |
| 10 | MaskGAN: Towards Diverse and Interactive Facial Image Manipulation Hit paper breakdown → | 2020 | 800 |
| 11 | Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net Hit paper breakdown → | 2018 | 686 |
| 12 | A large-scale car dataset for fine-grained categorization and verification Hit paper breakdown → | 2015 | 658 |
| 13 | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Hit paper breakdown → | 2021 | 654 |
| 14 | Semantic Image Segmentation via Deep Parsing Network Hit paper breakdown → | 2015 | 516 |
| 15 | PolarMask: Single Shot Instance Segmentation With Polar Representation Hit paper breakdown → | 2020 | 512 |
| 16 | Deep Learning Strong Parts for Pedestrian Detection Hit paper breakdown → | 2015 | 426 |
| 17 | From Facial Parts Responses to Face Detection: A Deep Learning Approach Hit paper breakdown → | 2015 | 423 |
| 18 | DiffusionDet: Diffusion Model for Object Detection Hit paper breakdown → | 2023 | 407 |
| 19 | DeepID-Net: Deformable deep convolutional neural networks for object detection Hit paper breakdown → | 2015 | 349 |
| 20 | Pedestrian Attribute Recognition At Far Distance Hit paper breakdown → | 2014 | 336 |
About Ping Luo
Ping Luo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Automotive Engineering, Control and Systems Engineering and Computational Mechanics, having authored 260 papers that have together received 35.7k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (94 papers), Generative Adversarial Networks and Image Synthesis (22 papers), Multimodal Machine Learning Applications (21 papers), Human Pose and Action Recognition (20 papers), Domain Adaptation and Few-Shot Learning (19 papers), Face recognition and analysis (17 papers), Autonomous Vehicle Technology and Safety (12 papers) and 3D Shape Modeling and Analysis (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (26.1k citations), Automotive Engineering (1.5k citations), Artificial Intelligence (5k citations), Media Technology (1.2k citations) and Signal Processing (584 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, Chen Change Loy, Enze Xie, Wenhai Wang, Ding Liang, Tong Lü, Xiang Li and Ling Shao. 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, Lecture notes in computer science and IEEE Transactions on Neural Networks and Learning Systems.
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.