Ismail Elezi
Impact in
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- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Advanced Vision and Imaging
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- Music and Audio Processing
Papers in
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- Face recognition and analysis 2
- Video Surveillance and Tracking Methods 2
- Advanced Neural Network Applications 2
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- Music and Audio Processing 3
- Speech and Audio Processing 2
- Co-authors
- Laura Leal-Taixé (3 shared papers)Guillem Brasó (1 shared paper)Zhiding Yu (1 shared paper)Anima Anandkumar (1 shared paper)Jose M. Álvarez (1 shared paper)Marcello Pelillo (4 shared papers)Thilo Stadelmann (3 shared papers)Marco Fiorucci (1 shared paper)
- Journals
- Pattern Recognition Letters (2 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)ZHAW Digital Collection (1 paper)
- Partner nations
- United KingdomItalySwitzerland
In The Last Decade
Ismail Elezi
9 papers receiving 113 citations
Peers
Comparison fields: 5 of 35
- Computer Vision and Pattern Recognition 88
- Signal Processing 14
- Artificial Intelligence 32
- Aerospace Engineering 24
- Media Technology 6
Countries citing papers authored by Ismail Elezi
This map shows the geographic impact of Ismail Elezi'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 Ismail Elezi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ismail Elezi more than expected).
Fields of papers citing papers by Ismail Elezi
This network shows the impact of papers produced by Ismail Elezi. 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 Ismail Elezi. The network helps show where Ismail Elezi may publish in the future.
Co-authors
The 16 scholars most cited alongside Ismail Elezi, 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 | 58 | |
| 2 | 2022 | 27 | |
| 3 | 2022 | 14 | |
| 4 | 2018 | 5 | |
| 5 | 2018 | 5 | |
| 6 | 2016 | 5 | |
| 7 | 2024 | 2 | |
| 8 | 2025 | 1 | |
| 9 | 2018 | 1 | |
| 10 | 2024 | 0 | |
| 11 | 2022 | 0 | |
| 12 | 2025 | 0 |
About Ismail Elezi
Ismail Elezi is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Discrete Mathematics and Combinatorics and Control and Systems Engineering, having authored 12 papers that have together received 118 indexed citations. Recurring topics across this work include Music and Audio Processing (3 papers), Machine Learning and Algorithms (2 papers), Face recognition and analysis (2 papers), Video Surveillance and Tracking Methods (2 papers), Advanced Neural Network Applications (2 papers), Speech and Audio Processing (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Graph Labeling and Dimension Problems (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (88 citations), Signal Processing (14 citations), Artificial Intelligence (32 citations), Aerospace Engineering (24 citations) and Media Technology (6 citations). Ismail Elezi has collaborated with scholars based in United Kingdom, Italy and Switzerland. Frequent co-authors include Laura Leal-Taixé, Guillem Brasó, Zhiding Yu, Anima Anandkumar, Jose M. Álvarez, Marcello Pelillo, Thilo Stadelmann, Marco Fiorucci, Sebastiano Vascon and Jürgen Schmidhuber. Their work appears in journals such as Pattern Recognition Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Proceedings of the AAAI Conference on Artificial Intelligence and ZHAW Digital Collection.
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.