28 citations · 63 across the 10 of their papers we have counts for
15 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…
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Paolo Testolina, Francesco Barbato, Umberto Michieli +3
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry…
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…
Prototype Guided Federated Learning of Visual Feature Representations
Umberto Michieli, Mete Ozay
Federated Learning (FL) is a framework which enables distributed model training using a large corpus of decentralized training data. Existing methods aggregate models disregarding…