Marco Cannici
- Human-Computer Interaction top 5%
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- Advanced Neural Network Applications 3
- Advanced Vision and Imaging 2
- Advanced Image and Video Retrieval Techniques 2
- Artificial Intelligence top 10%
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- Advanced Memory and Neural Computing 6
- CCD and CMOS Imaging Sensors 3
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- Robotics and Sensor-Based Localization 3
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- 3D Surveying and Cultural Heritage 2
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- Neural dynamics and brain function 1
- Co-authors
- Matteo MatteucciChiara PlizzariMarco CicconeBarbara CaputoDavide ScaramuzzaMathias GehrigDaniel GehrigA. Bottino
- Journals
- Computer Vision and Image Understanding (1 paper)Advances in Space Research (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- SwitzerlandItalyCanada
In The Last Decade
Marco Cannici
12 papers receiving 362 citations
Hit Papers
Peers
Comparison fields: 5 of 53
- Human-Computer Interaction 85
- Computer Vision and Pattern Recognition 287
- Artificial Intelligence 161
- Biomedical Engineering 127
- Acoustics and Ultrasonics 2
Countries citing papers authored by Marco Cannici
This map shows the geographic impact of Marco Cannici'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 Marco Cannici with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Cannici more than expected).
Fields of papers citing papers by Marco Cannici
This network shows the impact of papers produced by Marco Cannici. 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 Marco Cannici. The network helps show where Marco Cannici may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Marco Cannici, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 13 | |
| 8 | 2023 | 8 | |
| 9 | 2022 | 35 | |
| 10 | 2022 | 4 | |
| 11 | Skeleton-based action recognition via spatial and temporal transformer networksbreakdown → | 2021 | 253 |
| 12 | 2021 | 7 | |
| 13 | Matrix-LSTM: a Differentiable Recurrent Surface for Asynchronous Event-Based Data. | 2020 | 4 |
| 14 | 2019 | 29 | |
| 15 | Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras. | 2018 | 15 |
About Marco Cannici
Marco Cannici is a scholar working on Computer Vision and Pattern Recognition, Geology and Computer Graphics and Computer-Aided Design, having authored 15 papers that have together received 376 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (6 papers), CCD and CMOS Imaging Sensors (3 papers), Advanced Neural Network Applications (3 papers), Robotics and Sensor-Based Localization (3 papers), Advanced Vision and Imaging (2 papers), 3D Surveying and Cultural Heritage (2 papers), Advanced Image and Video Retrieval Techniques (2 papers) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Human-Computer Interaction (85 citations), Computer Vision and Pattern Recognition (287 citations) and Artificial Intelligence (161 citations). Marco Cannici has collaborated with scholars based in Switzerland, Italy and Canada. Frequent co-authors include Matteo Matteucci, Chiara Plizzari, Marco Ciccone, Barbara Caputo, Davide Scaramuzza, Mathias Gehrig, Daniel Gehrig, A. Bottino, Laurent Kneip and Ling Gao. Their work appears in journals such as Computer Vision and Image Understanding, Advances in Space Research 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.