Lorenzo Garattoni
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Mechanical Engineering
- Computer Networks and Communications top 10%
- Biomedical Engineering
- Co-authors
- Mauro BirattariGianpiero FrancescaRui DaiLuca MinciulloSrijan DasManuele BrambillaCarlo PinciroliFrançois Brémond
- Topics
- Human Pose and Action Recognition (8 papers)Anomaly Detection Techniques and Applications (7 papers)Modular Robots and Swarm Intelligence (5 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE AccessInternational Journal of Computer Vision
- Partner nations
- BelgiumFranceSwitzerland
In The Last Decade
Lorenzo Garattoni
21 papers receiving 410 citations
Peers
Comparison fields: 5 of 59
- Computer Vision and Pattern Recognition 216
- Artificial Intelligence 167
- Mechanical Engineering 129
- Computer Networks and Communications 115
- Biomedical Engineering 87
Countries citing papers authored by Lorenzo Garattoni
This map shows the geographic impact of Lorenzo Garattoni'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 Lorenzo Garattoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lorenzo Garattoni more than expected).
Fields of papers citing papers by Lorenzo Garattoni
This network shows the impact of papers produced by Lorenzo Garattoni. 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 Lorenzo Garattoni. The network helps show where Lorenzo Garattoni may publish in the future.
Co-authorship network of co-authors of Lorenzo Garattoni
This figure shows the co-authorship network connecting the top 25 collaborators of Lorenzo Garattoni. A scholar is included among the top collaborators of Lorenzo Garattoni based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lorenzo Garattoni. Lorenzo Garattoni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 6 | |
| 3 | 0 | |
| 4 | 8 | |
| 5 | 5 | |
| 6 | 2 | |
| 7 | 6 | |
| 8 | 24 | |
| 9 | 20 | |
| 10 | 15 | |
| 11 | 5 | |
| 12 | 6 | |
| 13 | 48 | |
| 14 | 101 | |
| 15 | 49 | |
| 16 | 14 | |
| 17 | 20 | |
| 18 | An Experiment in Automatic Design of Robot Swarms AutoMoDe-Vanilla, EvoStick, and Human Experts | 5 |
| 19 | 1 | |
| 20 | 2 |
About Lorenzo Garattoni
Lorenzo Garattoni is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Artificial Intelligence, having authored 22 papers that have together received 421 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (8 papers), Anomaly Detection Techniques and Applications (7 papers) and Modular Robots and Swarm Intelligence (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (216 citations), Human-Computer Interaction (38 citations) and Artificial Intelligence (167 citations). Lorenzo Garattoni has collaborated with scholars based in Belgium, France and Switzerland. Frequent co-authors include Mauro Birattari, Gianpiero Francesca, Rui Dai, Luca Minciullo, Srijan Das, Manuele Brambilla, Carlo Pinciroli, François Brémond, Andreagiovanni Reina and Arne Brutschy. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and International Journal of Computer Vision.
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.