Cijo Jose
Impact in
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- Video Surveillance and Tracking Methods
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Face and Expression Recognition
- Advanced Vision and Imaging
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- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
Papers in
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- Anomaly Detection Techniques and Applications 2
- Machine Learning and Data Classification 1
- Target Tracking and Data Fusion in Sensor Networks 1
- Neural Networks and Applications 1
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- Advanced Image and Video Retrieval Techniques 2
- Face and Expression Recognition 1
- Video Surveillance and Tracking Methods 1
- Multimodal Machine Learning Applications 1
- Co-authors
- François Fleuret (2 shared papers)Manik Varma (1 shared paper)Prasoon Goyal (1 shared paper)Tatjana Chavdarova (1 shared paper)Timur Bagautdinov (1 shared paper)Pascal Fua (1 shared paper)Luc Van Gool (1 shared paper)Pierre Baqué (1 shared paper)
- Journals
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (2 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- SwitzerlandIndia
In The Last Decade
Cijo Jose
5 papers receiving 185 citations
Peers
Comparison fields: 5 of 48
- Computer Vision and Pattern Recognition 130
- Artificial Intelligence 77
- Automotive Engineering 23
- Computational Mathematics 1
- Aerospace Engineering 26
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 | 121 | |
| 2 | Local Deep Kernel Learning for Efficient Non-linear SVM Prediction | 2013 | 53 |
| 3 | Importance sampling tree for large-scale empirical expectation | 2016 | 7 |
| 4 | Classification and Prediction of Wind Tunnel Mach Number Responses Using Both Competitive and Gamma Neural Networks | 1995 | 6 |
| 5 | 2025 | 1 |
About Cijo Jose
Cijo Jose is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 188 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (2 papers), Anomaly Detection Techniques and Applications (2 papers), Machine Learning and Data Classification (1 paper), Face and Expression Recognition (1 paper), Target Tracking and Data Fusion in Sensor Networks (1 paper), Video Surveillance and Tracking Methods (1 paper), Multimodal Machine Learning Applications (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (130 citations), Artificial Intelligence (77 citations), Automotive Engineering (23 citations), Computational Mathematics (1 citation) and Aerospace Engineering (26 citations). Cijo Jose has collaborated with scholars based in Switzerland and India. Frequent co-authors include François Fleuret, Manik Varma, Prasoon Goyal, Tatjana Chavdarova, Timur Bagautdinov, Pascal Fua, Luc Van Gool, Pierre Baqué, Andrii Maksai and Marc Szafraniec. Their work appears in journals such as Infoscience (Ecole Polytechnique Fédérale de Lausanne) and International Conference on Machine Learning.
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.