Alexandre Ramé
Impact in
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Anomaly Detection Techniques and Applications
- Topic Modeling
Papers in
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- Advanced Neural Network Applications 2
- Image Enhancement Techniques 1
- Multimodal Machine Learning Applications 1
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- Domain Adaptation and Few-Shot Learning 2
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Matthieu Cord (3 shared papers)Arthur Douillard (2 shared papers)Guillaume Couairon (1 shared paper)Nicolas Thome (1 shared paper)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2 papers)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
In The Last Decade
Alexandre Ramé
4 papers receiving 206 citations
Alexandre Ramé's Hit Papers
Peers
Comparison fields: 5 of 41
- Computer Vision and Pattern Recognition 133
- Artificial Intelligence 182
- Radiology, Nuclear Medicine and Imaging 16
- Media Technology 6
- Neurology 4
Countries citing papers authored by Alexandre Ramé
This map shows the geographic impact of Alexandre Ramé'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 Alexandre Ramé with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexandre Ramé more than expected).
Fields of papers citing papers by Alexandre Ramé
This network shows the impact of papers produced by Alexandre Ramé. 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 Alexandre Ramé. The network helps show where Alexandre Ramé may publish in the future.
Co-authors
The 4 scholars most cited alongside Alexandre Ramé, 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 | DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion Hit paper breakdown → | 2022 | 181 |
| 2 | 2021 | 26 | |
| 3 | 2022 | 2 | |
| 4 | 2022 | 2 |
About Alexandre Ramé
Alexandre Ramé is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Social Psychology, Atomic and Molecular Physics, and Optics and Signal Processing, having authored 4 papers that have together received 211 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Image Enhancement Techniques (1 paper), Speech and Audio Processing (1 paper), Adversarial Robustness in Machine Learning (1 paper), Multimodal Machine Learning Applications (1 paper), Sparse and Compressive Sensing Techniques (1 paper) and Advanced MIMO Systems Optimization (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (133 citations), Artificial Intelligence (182 citations), Radiology, Nuclear Medicine and Imaging (16 citations), Media Technology (6 citations) and Neurology (4 citations). Alexandre Ramé has collaborated with scholars based in France and Australia. Frequent co-authors include Matthieu Cord, Arthur Douillard, Guillaume Couairon and Nicolas Thome. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.