28 citations · 37 across the 7 of their papers we have counts for
9 papers
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…