48 citations · 138 across the 24 of their papers we have counts for
5 papers · 1 filter
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adapt…
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
Umberto Michieli, Edoardo Borsato, Luca Rossi +1
The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in t…
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
Marco Toldo, Andrea Maracani, Umberto Michieli +1
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attract…
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training
Teo Spadotto, Marco Toldo, Umberto Michieli +1
Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled d…
Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
Marco Toldo, Umberto Michieli, Gianluca Agresti +1
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use…