3 papers
cs.AI2026
OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
Chiara Maria Russo, Simone Carnemolla, Simone Palazzo +3
Interpreting the decisions of deep image classifiers remains challenging, particularly in black-box settings where model internals are inaccessible. We introduce OCCAM, a framework…
cs.CV2026
UNBOX: Unveiling Black-box visual models with Natural-language
Simone Carnemolla, Chiara Russo, Simone Palazzo +5
Ensuring trustworthiness in open-world visual recognition requires models that are interpretable, fair, and robust to distribution shifts. Yet modern vision systems are increasingl…
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…