Devis Tuia

Wasserstein Adversarial Regularization (WAR) on label noise

2019

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve …

Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward

2023 · arXiv (Cornell University)

Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's eye view of the essential …

Self-supervised Change Detection via Cooperative Learning: A Two-Player Model

2025

Change detection (CD) is a critical task in remote sensing, especially for monitoring dynamic urban environments. In this work, we present a novel, self-supervised CD approach that leverages a two-player architecture to improve the reliability …

TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation

2025 · arXiv (Cornell University)

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 …