16 citations · 28 across the 19 of their papers we have counts for
8 papers · 1 filter
Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks
Eugenio Lomurno, Matteo Matteucci
Generative artificial intelligence has transformed the generation of synthetic data, providing innovative solutions to challenges like data scarcity and privacy, which are particul…
The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry
Paolo Cudrano, Xiaoyu Luo, Matteo Matteucci
As robotics continues to advance, the need for adaptive and continuously-learning embodied agents increases, particularly in the realm of assistance robotics. Quick adaptability an…
More than the Sum of Its Parts: Ensembling Backbone Networks for Few-Shot Segmentation
Nico Catalano, Alessandro Maranelli, Agnese Chiatti +1
Semantic segmentation is a key prerequisite to robust image understanding for applications in \acrlong{ai} and Robotics. \acrlong{fss}, in particular, concerns the extension and op…
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?
Cristian Sbrolli, Paolo Cudrano, Matteo Matteucci
Recent advancements in deep generative models, particularly with the application of CLIP (Contrastive Language Image Pretraining) to Denoising Diffusion Probabilistic Models (DDPMs…
Continual Cross-Dataset Adaptation in Road Surface Classification
Paolo Cudrano, Matteo Bellusci, Giuseppe Macino +1
Accurate road surface classification is crucial for autonomous vehicles (AVs) to optimize driving conditions, enhance safety, and enable advanced road mapping. However, deep learni…
Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques
Andrea Lampis, Eugenio Lomurno, Matteo Matteucci
Acquiring and annotating suitable datasets for training deep learning models is challenging. This often results in tedious and time-consuming efforts that can hinder research progr…