20 citations · 24 across the 8 of their papers we have counts for
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cs.CV2022★ 3 cited
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Donald Shenaj, Eros Fanì, Marco Toldo +6
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the co…
cs.CV2022★ 20 cited
Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation
Donald Shenaj, Francesco Barbato, Umberto Michieli +1
Deep neural networks are typically trained in a single shot for a specific task and data distribution, but in real world settings both the task and the domain of application can ch…