48 citations · 85 across the 10 of their papers we have counts for
10 papers
Asynchronous Federated Continual Learning
Donald Shenaj, Marco Toldo, Alberto Rigon +1
The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and predefined order. This is not very realistic in federated l…
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains
Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep learning models dealing with image understanding in real-world settings must be able to adapt to a wide variety of tasks across different domains. Domain adaptation and class…
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…
RECALL: Replay-based Continual Learning in Semantic Segmentation
Andrea Maracani, Umberto Michieli, Marco Toldo +1
Deep networks allow to obtain outstanding results in semantic segmentation, however they need to be trained in a single shot with a large amount of data. Continual learning setting…
Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations
Francesco Barbato, Umberto Michieli, Marco Toldo +1
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training. Additionally, such models do…
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
Francesco Barbato, Marco Toldo, Umberto Michieli +1
Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to…
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…
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…