Umberto Michieli
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Radiology, Nuclear Medicine and Imaging top 10%
- Media Technology top 10%
- Environmental Engineering
- Co-authors
- Pietro ZanuttighMarco ToldoGianluca AgrestiMete ÖzayMichele ZorziMarco GiordaniSimone MilaniMarco Ciccone
- Topics
- Domain Adaptation and Few-Shot Learning (20 papers)Advanced Neural Network Applications (17 papers)Multimodal Machine Learning Applications (10 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceRadiology, Nuclear Medicine and Imaging
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine IntelligenceInternational Journal of Computer Vision
- Partner nations
- ItalyUnited KingdomSouth Korea
In The Last Decade
Umberto Michieli
30 papers receiving 771 citations
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 559
- Computer Vision and Pattern Recognition 552
- Radiology, Nuclear Medicine and Imaging 146
- Media Technology 44
- Environmental Engineering 36
Countries citing papers authored by Umberto Michieli
This map shows the geographic impact of Umberto Michieli'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 Umberto Michieli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Umberto Michieli more than expected).
Fields of papers citing papers by Umberto Michieli
This network shows the impact of papers produced by Umberto Michieli. 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 Umberto Michieli. The network helps show where Umberto Michieli may publish in the future.
Co-authorship network of co-authors of Umberto Michieli
This figure shows the co-authorship network connecting the top 25 collaborators of Umberto Michieli. A scholar is included among the top collaborators of Umberto Michieli 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 Umberto Michieli. Umberto Michieli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 28 | |
| 9 | 4 | |
| 10 | 1 | |
| 11 | 15 | |
| 12 | 7 | |
| 13 | 39 | |
| 14 | 91 | |
| 15 | 51 | |
| 16 | 22 | |
| 17 | 41 | |
| 18 | 142 | |
| 19 | 30 | |
| 20 | 11 |
About Umberto Michieli
Umberto Michieli is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Science Applications, having authored 32 papers that have together received 778 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (20 papers), Advanced Neural Network Applications (17 papers) and Multimodal Machine Learning Applications (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (552 citations), Artificial Intelligence (559 citations) and Radiology, Nuclear Medicine and Imaging (146 citations). Umberto Michieli has collaborated with scholars based in Italy, United Kingdom and South Korea. Frequent co-authors include Pietro Zanuttigh, Marco Toldo, Gianluca Agresti, Mete Özay, Michele Zorzi, Marco Giordani, Simone Milani, Marco Ciccone, Barbara Caputo and Andrea Giordano. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence 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.