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Matteo Mosconi

2 accepted papers

2025

Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning

ICLR 2025poster

Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving performance and scalability. In this respect, the compositional properties of low-rank adaptation techniques (e.g., LoRA) have proven beneficial, as s…

2025

DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection

NeurIPS 2025poster

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 their abilities on multiple specialized domains remains essential. While recent methods rely on monolithic adaptation s…

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