6 papers
ABRA: Teleporting Fine-Tuned Knowledge Across Domains for Open-Vocabulary Object Detection
Mattia Bernardi, Chiara Cappellino, Matteo Mosconi +3
Although recent Open-Vocabulary Object Detection architectures, such as Grounding DINO, demonstrate strong zero-shot capabilities, their performance degrades significantly under do…
Intrinsic Training Signals for Federated Learning Aggregation
Cosimo Fiorini, Matteo Mosconi, Pietro Buzzega +2
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. While existing approaches for aggregating client-specific cla…
DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
Chiara Cappellino, Gianluca Mancusi, Matteo Mosconi +3
Open-Vocabulary object detectors can generalize to an unrestricted set of categories through simple textual prompting. However, adapting these models to rare classes or reinforcing…
Closed-form merging of parameter-efficient modules for Federated Continual Learning
Riccardo Salami, Pietro Buzzega, Matteo Mosconi +3
Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving perfor-mance and scalability.…
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
Matteo Mosconi, Andriy Sorokin, Aniello Panariello +6
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of…
Federated Class-Incremental Learning with Hierarchical Generative Prototypes
Riccardo Salami, Pietro Buzzega, Matteo Mosconi +2
Federated Learning (FL) aims at unburdening the training of deep models by distributing computation across multiple devices (clients) while safeguarding data privacy. On top of tha…