activity
20222024
most citedRadio astronomical images object detection and segmentation: A benchmark on deep learning methods

18 citations · 26 across the 19 of their papers we have counts for

collaborators

19 papers

astro-ph.IM20241 cited

Self-supervised learning for radio-astronomy source classification: a benchmark

Thomas Cecconello, Simone Riggi, Ugo Becciani +5

The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional…

cs.CV2024

Evidential Federated Learning for Skin Lesion Image Classification

Rutger Hendrix, Federica Proietto Salanitri, Concetto Spampinato +2

We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesio…

cs.LG2024

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.…

eess.IV20241 cited

IPMN Risk Assessment under Federated Learning Paradigm

Hongyi Pan, Ziliang Hong, Gorkem Durak +17

Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop…

cs.CV2024

SalFoM: Dynamic Saliency Prediction with Video Foundation Models

Morteza Moradi, Mohammad Moradi, Francesco Rundo +3

Recent advancements in video saliency prediction (VSP) have shown promising performance compared to the human visual system, whose emulation is the primary goal of VSP. However, cu…

cs.CV2024

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…