2 citations · 3 across the 2 of their papers we have counts for
3 papers
Muting Tokens: Exploring Token Sparsification for Efficient Audio Transformers
Shreya Sajid, Fabrizio Julian Piva, Hiram Rayo Torres Rodriguez +3
Transformer-based models have shown strong performance across diverse tasks, but their reliance on self-attention mechanisms often leads to high computational costs. Token sparsifi…
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…
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…