7 papers
TASSO: TAsk-Specific Subspace Optimization for Continual Learning of Vision-Language Models
Chang Sun, Francesco Barbato, Matteo Caligiuri +1
Vision-Language Models (VLMs) exhibit strong zero-shot capabilities, making them an attractive solution for continual learning across diverse tasks. However, during continual adapt…
EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models
Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh +1
Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training. Nevertheless, increasing model sizes impose subs…
Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation
Martina Pavan, Matteo Caligiuri, Francesco Barbato +1
Exploiting deep learning in medical imaging faces critical challenges, including strict privacy constraints, heterogeneous imaging devices with varying acquisition properties, and…
FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Models
Matteo Caligiuri, Francesco Barbato, Donald Shenaj +2
Federated Learning (FL) is an established paradigm for training deep learning models on decentralized data. However, as the size of the models grows, conventional FL approaches oft…
FlyAwareV2: A Multimodal Cross-Domain UAV Dataset for Urban Scene Understanding
Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
The development of computer vision algorithms for Unmanned Aerial Vehicle (UAV) applications in urban environments heavily relies on the availability of large-scale datasets with a…
NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data
Matteo Caligiuri, Adriano Simonetto, Pietro Zanuttigh
The acquisition of objects outside the Line-of-Sight of cameras is a very intriguing but also extremely challenging research topic. Recent works showed the feasibility of this idea…