18 citations · 33 across the 5 of their papers we have counts for
5 papers
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation
Renato Sortino, Thomas Cecconello, Andrea DeMarco +10
Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from…
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
Renato Sortino, Daniel Magro, Giuseppe Fiameni +9
In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being e…
Astronomical source detection in radio continuum maps with deep neural networks
S. Riggi, D. Magro, R. Sortino +13
Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Austra…
Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets
Marcella Cornia, Lorenzo Baraldi, Giuseppe Fiameni +1
This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources, containing both human-annotated and web-collected captions…
From Show to Tell: A Survey on Deep Learning-based Image Captioning
Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi +3
Connecting Vision and Language plays an essential role in Generative Intelligence. For this reason, large research efforts have been devoted to image captioning, i.e. describing im…