Gabriele Graffieti

401 citations
9 papers · 133 indexed · h-index 6
Topics
Domain Adaptation and Few-Shot Learning (5 papers)Advanced Vision and Imaging (2 papers)Advanced Image Processing Techniques (2 papers)
Partner nations
Italy

In The Last Decade

Gabriele Graffieti

8 papers receiving 129 citations

Peers

Gabriele Graffieti
Comparison fields: 5 of 38
  • Artificial Intelligence 93
  • Computer Vision and Pattern Recognition 72
  • Radiology, Nuclear Medicine and Imaging 13
  • Signal Processing 12
  • Biomedical Engineering 7
Replace Lorenzo Pellegrini with:
Lorenzo Pellegrini Italy
Jiannan Wu Hong Kong
Liangchen Luo China
Marcin Moczulski United Kingdom
Gunnar A. Sigurdsson United States
Evgeniya Ustinova Russia
Davide Testuggine United States
Vighnesh Birodkar United States
Chuangchuang Tan China
Gabriele Graffieti relative to Lorenzo Pellegrini Italy Lorenzo Pellegrini's profile →
Citations per field
00.5×1.5×
Lorenzo Pellegrini · 1×
Citations per year

Countries citing papers authored by Gabriele Graffieti

Since Specialization
Citations

This map shows the geographic impact of Gabriele Graffieti'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 Gabriele Graffieti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriele Graffieti more than expected).

Fields of papers citing papers by Gabriele Graffieti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriele Graffieti. 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 Gabriele Graffieti. The network helps show where Gabriele Graffieti may publish in the future.

Co-authorship network of co-authors of Gabriele Graffieti

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriele Graffieti. A scholar is included among the top collaborators of Gabriele Graffieti 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 Gabriele Graffieti. Gabriele Graffieti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
#WorkIndexed citations
1 12
2 15
3 13
4 0
5 6
6 12
7 1
8 2
9 72

About Gabriele Graffieti

Gabriele Graffieti is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 9 papers that have together received 133 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Advanced Vision and Imaging (2 papers) and Advanced Image Processing Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (72 citations), Artificial Intelligence (93 citations) and Signal Processing (12 citations). Gabriele Graffieti has collaborated with scholars based in Italy. Frequent co-authors include Davide Maltoni, Vincenzo Lomonaco, Lorenzo Pellegrini, Guido Borghi, Annalisa Franco, Andrea Cossu, Antonio Carta and Davide Bacciu. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Neural Networks.

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.

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