Mattia Litrico — accepted AI-conference papers

2026

TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation

Mattia Litrico, Mario Valerio Giuffrida, Sebastiano Battiato, Devis Tuia

AAAI 2026technical

Recent unsupervised domain adaptation (UDA) methods have shown great success in addressing classical domain shifts (e.g., synthetic-to-real), but they still suffer under complex shifts (e.g. geographical shift), where both the background and object appearances differ significantly across domains. Pr

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2023

Guiding Pseudo-Labels With Uncertainty Estimation for Source-Free Unsupervised Domain Adaptation

Mattia Litrico, Alessio Del Bue, Pietro Morerio

CVPR 2023poster

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to s…

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