Assaf Arbelle
Impact in
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Topic Modeling
Papers in
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- Multimodal Machine Learning Applications 5
- Advanced Vision and Imaging 2
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- Domain Adaptation and Few-Shot Learning 6
- Topic Modeling 2
- Adversarial Robustness in Machine Learning 2
- Explainable Artificial Intelligence (XAI) 1
- Co-authors
- Leonid Karlinsky (9 shared papers)Rogério Feris (5 shared papers)Rameswar Panda (3 shared papers)James Smith (2 shared papers)Zsolt Kira (2 shared papers)Paola Cascante-Bonilla (2 shared papers)Donghyun Kim (2 shared papers)Tammy Riklin Raviv (2 shared papers)
- Journals
- Medical Image Analysis (1 paper)Computer Vision and Image Understanding (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- United StatesIsraelGermany
In The Last Decade
Assaf Arbelle
10 papers receiving 192 citations
Peers
Comparison fields: 5 of 54
- Computer Vision and Pattern Recognition 102
- Artificial Intelligence 117
- Biophysics 15
- Media Technology 22
- Signal Processing 9
Countries citing papers authored by Assaf Arbelle
This map shows the geographic impact of Assaf Arbelle'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 Assaf Arbelle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Assaf Arbelle more than expected).
Fields of papers citing papers by Assaf Arbelle
This network shows the impact of papers produced by Assaf Arbelle. 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 Assaf Arbelle. The network helps show where Assaf Arbelle may publish in the future.
Co-authors
The 25 scholars most cited alongside Assaf Arbelle, 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 | 2023 | 93 | |
| 2 | 2018 | 21 | |
| 3 | 2023 | 19 | |
| 4 | 2022 | 18 | |
| 5 | 2024 | 16 | |
| 6 | 2023 | 11 | |
| 7 | 2023 | 7 | |
| 8 | 2023 | 6 | |
| 9 | 2024 | 2 | |
| 10 | 2024 | 2 | |
| 11 | 2024 | 0 |
About Assaf Arbelle
Assaf Arbelle is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Theory and Mathematics, Media Technology and Biophysics, having authored 11 papers that have together received 195 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (6 papers), Multimodal Machine Learning Applications (5 papers), Advanced Vision and Imaging (2 papers), Topic Modeling (2 papers), Adversarial Robustness in Machine Learning (2 papers), Explainable Artificial Intelligence (XAI) (1 paper), Cell Image Analysis Techniques (1 paper) and Image Processing Techniques and Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (102 citations), Artificial Intelligence (117 citations), Biophysics (15 citations), Media Technology (22 citations) and Signal Processing (9 citations). Assaf Arbelle has collaborated with scholars based in United States, Israel and Germany. Frequent co-authors include Leonid Karlinsky, Rogério Feris, Rameswar Panda, James Smith, Zsolt Kira, Paola Cascante-Bonilla, Donghyun Kim, Tammy Riklin Raviv, Eli Schwartz and Raja Giryes. Their work appears in journals such as Medical Image Analysis, Computer Vision and Image Understanding, IEEE Transactions on Pattern Analysis and Machine Intelligence and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.