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…
MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
Elena Camuffo, Francesco Barbato, Mete Ozay +2
Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples. Lightweight models often struggle in this sett…
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…
Split&Splat: Zero-Shot Panoptic Segmentation via Explicit Instance Modeling and 3D Gaussian Splatting
Leonardo Monchieri, Elena Camuffo, Francesco Barbato +2
3D Gaussian Splatting (GS) enables fast and high-quality scene reconstruction, but it lacks an object-consistent and semantically aware structure. We propose Split&Splat, a framewo…
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…