Mattia Segù
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Biomedical Engineering
- Control and Systems Engineering
- Co-authors
- Antonio LoquercioDavide ScaramuzzaFederico TombariFisher YuAlessio TonioniMuhammad Haris KhanArif MahmoodSeung‐Ik Lee
- Topics
- Domain Adaptation and Few-Shot Learning (4 papers)Advanced Vision and Imaging (3 papers)Video Surveillance and Tracking Methods (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceComputer Networks and Communications
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligencePattern RecognitionInternational Journal of Computer Vision
- Partner nations
- SwitzerlandUnited StatesGermany
In The Last Decade
Mattia Segù
9 papers receiving 528 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 302
- Computer Vision and Pattern Recognition 244
- Computer Networks and Communications 100
- Biomedical Engineering 55
- Control and Systems Engineering 50
Countries citing papers authored by Mattia Segù
This map shows the geographic impact of Mattia Segù'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 Mattia Segù with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mattia Segù more than expected).
Fields of papers citing papers by Mattia Segù
This network shows the impact of papers produced by Mattia Segù. 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 Mattia Segù. The network helps show where Mattia Segù may publish in the future.
Co-authorship network of co-authors of Mattia Segù
This figure shows the co-authorship network connecting the top 25 collaborators of Mattia Segù. A scholar is included among the top collaborators of Mattia Segù 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 Mattia Segù. Mattia Segù is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 32 | |
| 6 | 9 | |
| 7 | 3 | |
| 8 | 5 | |
| 9 | 88 | |
| 10 | 91 | |
| 11 | 131 | |
| 12 | 182 |
About Mattia Segù
Mattia Segù is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 12 papers that have together received 544 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Advanced Vision and Imaging (3 papers) and Video Surveillance and Tracking Methods (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (244 citations), Artificial Intelligence (302 citations) and Computer Networks and Communications (100 citations). Mattia Segù has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Antonio Loquercio, Davide Scaramuzza, Federico Tombari, Fisher Yu, Alessio Tonioni, Muhammad Haris Khan, Arif Mahmood, Seung‐Ik Lee, Muhammad Zaigham Zaheer and Luc Van Gool. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition 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.