Federico Landi
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computational Mechanics
- Co-authors
- Rita CucchiaraMarcella CorniaLorenzo BaraldiMassimiliano CorsiniSilvia CascianelliRoberto BigazziGianandrea La PortaGiacomo Assandri
- Topics
- Multimodal Machine Learning Applications (4 papers)Generative Adversarial Networks and Image Synthesis (3 papers)3D Shape Modeling and Analysis (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignArtificial Intelligence
- Partner nations
- Italy
In The Last Decade
Federico Landi
9 papers receiving 275 citations
Peers
Comparison fields: 5 of 85
- Computer Vision and Pattern Recognition 118
- Artificial Intelligence 67
- Electrical and Electronic Engineering 33
- Control and Systems Engineering 27
- Computational Mechanics 27
Countries citing papers authored by Federico Landi
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).
Fields of papers citing papers by Federico Landi
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.
Co-authorship network of co-authors of Federico Landi
This figure shows the co-authorship network connecting the top 25 collaborators of Federico Landi. A scholar is included among the top collaborators of Federico Landi 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 Federico Landi. Federico Landi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 12 | |
| 3 | 48 | |
| 4 | 14 | |
| 5 | 3 | |
| 6 | 163 | |
| 7 | 14 | |
| 8 | 22 | |
| 9 | Embodied Vision-and-Language Navigation with Dynamic Convolutional Filters | 3 |
About Federico Landi
Federico Landi is a scholar working on Computer Vision and Pattern Recognition, Ecological Modeling and Computational Mechanics, having authored 9 papers that have together received 281 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and 3D Shape Modeling and Analysis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (118 citations), Computer Graphics and Computer-Aided Design (14 citations) and Artificial Intelligence (67 citations). Federico Landi has collaborated with scholars based in Italy. Frequent co-authors include Rita Cucchiara, Marcella Cornia, Lorenzo Baraldi, Massimiliano Corsini, Silvia Cascianelli, Roberto Bigazzi, Gianandrea La Porta and Giacomo Assandri. Their work appears in journals such as Neural Networks, Computer Vision and Image Understanding and IEEE Robotics and Automation Letters.
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.