19 citations · 38 across the 19 of their papers we have counts for
19 papers
Retrieval-Augmented Visual Prompting: Guiding Foundation Models in Two-Photon Imaging
Salvatore Calcagno, Marco Finocchiaro, Giovanni Bellitto +3
Two-photon calcium imaging presents a challenging setting for foundation models: image appearance varies substantially across recordings and experimental conditions, annotations ar…
TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations
Rutger Hendrix, Leonardo G. Russo, Concetto Spampinato +2
The demand for privacy-compliant AI has amplified the need for machine unlearning; yet, existing retraining or distillation-based methods remain unverifiable and computationally co…
Routing without Forgetting
Alessio Masano, Giovanni Bellitto, Dipam Goswani +2
Continual learning in transformers is commonly addressed through parameter-efficient adaptation: prompts, adapters, or LoRA modules are specialized per task while the backbone rema…
Dream2Learn: Structured Generative Dreaming for Continual Learning
Salvatore Calcagno, Matteo Pennisi, Federica Proietto Salanitri +4
Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting. Inspired by human dreaming as a mechanism for internal simulation and knowl…
DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe +5
Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework th…
Zero-Shot Decentralized Federated Learning
Alessio Masano, Matteo Pennisi, Federica Proietto Salanitri +2
CLIP has revolutionized zero-shot learning by enabling task generalization without fine-tuning. While prompting techniques like CoOp and CoCoOp enhance CLIP's adaptability, their e…
Pre-Forgettable Models: Prompt Learning as a Native Mechanism for Unlearning
Rutger Hendrix, Giovanni Patanè, Leonardo G. Russo +5
Foundation models have transformed multimedia analysis by enabling robust and transferable representations across diverse modalities and tasks. However, their static deployment con…
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning
Luca Palazzo, Matteo Pennisi, Federica Proietto Salanitri +3
In this paper, we present FedRewind, a novel approach to decentralized federated learning that leverages model exchange among nodes to address the issue of data distribution shift.…
Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models
Matteo Pennisi, Giovanni Bellitto, Simone Palazzo +2
We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability. DiffExplainer employs diffusion models conditioned on…
Selective Attention-based Modulation for Continual Learning
Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi +5
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting. Inspired by neurophysiolog…
Wake-Sleep Consolidated Learning
Amelia Sorrenti, Giovanni Bellitto, Federica Proietto Salanitri +3
We propose Wake-Sleep Consolidated Learning (WSCL), a learning strategy leveraging Complementary Learning System theory and the wake-sleep phases of the human brain to improve the…
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto +3
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using GA…
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Emanuele Frascaroli +6
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically. Continual Learning (CL) approaches seek to bridge this gap by facil…
TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation
Feiyan Hu, Simone Palazzo, Federica Proietto Salanitri +4
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications. However, current solutions are p…
Transfer without Forgetting
Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello +5
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretrainin…
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo +9
Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more f…
Effects of Auxiliary Knowledge on Continual Learning
Giovanni Bellitto, Matteo Pennisi, Simone Palazzo +4
In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information…
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto +4
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recen…
Hierarchical 3D Feature Learning for Pancreas Segmentation
Federica Proietto Salanitri, Giovanni Bellitto, Ismail Irmakci +3
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encode…