Cijo Jose
Impact in
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- Video Surveillance and Tracking Methods
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Face recognition and analysis
- Advanced Image and Video Retrieval Techniques
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- Anomaly Detection Techniques and Applications
Papers in
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- Advanced Image and Video Retrieval Techniques 2
- Video Surveillance and Tracking Methods 2
- Human Pose and Action Recognition 1
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- Anomaly Detection Techniques and Applications 2
- Domain Adaptation and Few-Shot Learning 1
- Adversarial Robustness in Machine Learning 1
- Neural Networks and Applications 1
- Target Tracking and Data Fusion in Sensor Networks 1
- Co-authors
- François Fleuret (3 shared papers)Luc Van Gool (1 shared paper)Manik Varma (1 shared paper)Andrii Maksai (1 shared paper)Tatjana Chavdarova (1 shared paper)Pascal Fua (1 shared paper)Pierre Baqué (1 shared paper)Prasoon Goyal (1 shared paper)
- Journals
- Lecture notes in computer science (1 paper)International Conference on Machine Learning (1 paper)Infoscience (Ecole Polytechnique Fédérale de Lausanne) (2 papers)
- Partner nations
- SwitzerlandIndia
In The Last Decade
Cijo Jose
6 papers receiving 322 citations
Peers
Comparison fields: 5 of 52
- Computer Vision and Pattern Recognition 262
- Artificial Intelligence 84
- Automotive Engineering 25
- Biomedical Engineering 74
- Computational Mathematics 1
Countries citing papers authored by Cijo Jose
This map shows the geographic impact of Cijo Jose'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 Cijo Jose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cijo Jose more than expected).
Fields of papers citing papers by Cijo Jose
This network shows the impact of papers produced by Cijo Jose. 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 Cijo Jose. The network helps show where Cijo Jose may publish in the future.
Co-authors
The 18 scholars most cited alongside Cijo Jose, 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 | 2018 | 139 | |
| 2 | 2016 | 115 | |
| 3 | Local Deep Kernel Learning for Efficient Non-linear SVM Prediction | 2013 | 56 |
| 4 | Importance sampling tree for large-scale empirical expectation | 2016 | 8 |
| 5 | Classification and Prediction of Wind Tunnel Mach Number Responses Using Both Competitive and Gamma Neural Networks | 1995 | 6 |
| 6 | 2025 | 3 |
About Cijo Jose
Cijo Jose is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Statistical and Nonlinear Physics, Biomedical Engineering and Infectious Diseases, having authored 6 papers that have together received 327 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Video Surveillance and Tracking Methods (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Adversarial Robustness in Machine Learning (1 paper), Neural Networks and Applications (1 paper), Human Pose and Action Recognition (1 paper) and Target Tracking and Data Fusion in Sensor Networks (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (262 citations), Artificial Intelligence (84 citations), Automotive Engineering (25 citations), Biomedical Engineering (74 citations) and Computational Mathematics (1 citation). Cijo Jose has collaborated with scholars based in Switzerland and India. Frequent co-authors include François Fleuret, Luc Van Gool, Manik Varma, Andrii Maksai, Tatjana Chavdarova, Pascal Fua, Pierre Baqué, Prasoon Goyal, Timur Bagautdinov and Huy V. Vo. Their work appears in journals such as Lecture notes in computer science, International Conference on Machine Learning and Infoscience (Ecole Polytechnique Fédérale de Lausanne).
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.