Federico Landi

9 papers and 188 indexed citations i.

About

Federico Landi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Federico Landi has authored 9 papers receiving a total of 188 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 3 papers in Computational Mechanics. Recurrent topics in Federico Landi’s work include Multimodal Machine Learning Applications (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and 3D Shape Modeling and Analysis (3 papers). Federico Landi is often cited by papers focused on Multimodal Machine Learning Applications (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and 3D Shape Modeling and Analysis (3 papers). Federico Landi collaborates with scholars based in Italy. Federico Landi's co-authors include Rita Cucchiara, Marcella Cornia, Lorenzo Baraldi, Massimiliano Corsini, Silvia Cascianelli, Roberto Bigazzi, Gianandrea La Porta and Giacomo Assandri and has published in prestigious journals such as Neural Networks, Computer Vision and Image Understanding and IEEE Robotics and Automation Letters.

In The Last Decade

Co-authorship network of co-authors of Federico Landi i

Fields of papers citing papers by Federico Landi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Federico Landi

Since Specialization
Citations

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

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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