no code implementations • 20 Mar 2024 • Giulia Rizzoli, Matteo Caligiuri, Donald Shenaj, Francesco Barbato, Pietro Zanuttigh
In Federated Learning (FL), multiple clients collaboratively train a global model without sharing private data.
1 code implementation • 28 Feb 2024 • Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel, Pietro Zanuttigh, Mete Ozay
To this end, we design a small, modular, and efficient (just 2GFLOPs to process a Full HD image) system to enhance input data for robust downstream multimedia understanding with minimal computational cost.
no code implementations • 19 Sep 2023 • Chang Liu, Giulia Rizzoli, Francesco Barbato, Andrea Maracani, Marco Toldo, Umberto Michieli, Yi Niu, Pietro Zanuttigh
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
1 code implementation • 21 Aug 2023 • Giulia Rizzoli, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs) imagery heavily relies on the availability of annotated high-resolution aerial data.
no code implementations • 9 Aug 2023 • Francesco Barbato, Elena Camuffo, Simone Milani, Pietro Zanuttigh
In this work, we re-frame the task of multimodal semantic segmentation by enforcing a tightly-coupled feature representation and a symmetric information-sharing scheme, which allows our approach to work even when one of the input modalities is missing.
no code implementations • 8 Nov 2022 • Francesco Barbato, Giulia Rizzoli, Pietro Zanuttigh
Most approaches for semantic segmentation use only information from color cameras to parse the scenes, yet recent advancements show that using depth data allows to further improve performances.
no code implementations • 20 Apr 2022 • Paolo Testolina, Francesco Barbato, Umberto Michieli, Marco Giordani, Pietro Zanuttigh, Michele Zorzi
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems.
no code implementations • 18 Jan 2022 • Donald Shenaj, Francesco Barbato, Umberto Michieli, Pietro Zanuttigh
In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift.
1 code implementation • 6 Aug 2021 • Francesco Barbato, Umberto Michieli, Marco Toldo, Pietro Zanuttigh
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training.
1 code implementation • 6 Apr 2021 • Francesco Barbato, Marco Toldo, Umberto Michieli, Pietro Zanuttigh
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 distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization.