2 citations · 2 across the 8 of their papers we have counts for
9 papers
Data Processing for Offline Evaluation in Recommender Systems: a Survey
Alberto Carlo Maria Mancino, Angela Di Fazio, Danilo Danese +5
Offline evaluation is the dominant experimental paradigm in recommender systems research, enabling reproducible and cost-effective comparisons on historical interaction data. Yet,…
Grounding Free-Form Instructions for Fashion Complementary Image Generation
Matteo Attimonelli, Claudio Pomo, Alessandro De Bellis +3
Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural multimodal grounding problem whe…
Who Are You Explaining To? A Multi-Agent System for Audience-Aware XAI Narratives
Francesco Musicco, Danilo Danese, Giuseppe Fasano +3
Feature-attribution methods such as SHAP provide useful evidence about individual model predictions, but their numerical outputs are rarely sufficient for audiences with different…
Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting
Danilo Danese, Angela Lombardi, Tommaso Di Noia
The ASNR-MICCAI BraTS Local Synthesis (Inpainting) task asks for the anatomically plausible completion of healthy brain tissue within a masked region of a T1-weighted MRI, providin…
WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis
Danilo Danese, Angela Lombardi, Giuseppe Fasano +2
Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data augmentation in this setting…
FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching
Danilo Danese, Angela Lombardi, Matteo Attimonelli +2
Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which esti…
Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
Claudio Pomo, Matteo Attimonelli, Danilo Danese +2
Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear…
Large-scale Benchmarks for Multimodal Recommendation with Ducho
Matteo Attimonelli, Danilo Danese, Angela Di Fazio +3
The common multimodal recommendation pipeline involves (i) extracting multimodal features, (ii) refining their high-level representations to suit the recommendation task, (iii) opt…
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation
Matteo Attimonelli, Danilo Danese, Daniele Malitesta +3
In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition a…