3 papers
cs.CV2025
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…
cs.SD2025
SeeingSounds: Learning Audio-to-Visual Alignment via Text
Simone Carnemolla, Matteo Pennisi, Chiara Russo +3
We introduce SeeingSounds, a lightweight and modular framework for audio-to-image generation that leverages the interplay between audio, language, and vision-without requiring any…
cs.LG2024
Back to Supervision: Boosting Word Boundary Detection through Frame Classification
Simone Carnemolla, Salvatore Calcagno, Simone Palazzo +1
Speech segmentation at both word and phoneme levels is crucial for various speech processing tasks. It significantly aids in extracting meaningful units from an utterance, thus ena…