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cs.CV2024★ 1 cited
Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation
Brunó B. Englert, Fabrizio J. Piva, Tommie Kerssies +2
Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safety-critical applications, where d…
cs.CV2021★ 2 cited
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation
Fabrizio J. Piva, Gijs Dubbelman
We introduce an unsupervised domain adaption (UDA) strategy that combines multiple image translations, ensemble learning and self-supervised learning in one coherent approach. We f…