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PERL: Parameter Efficient Reasoning in CLIP Latent Space
Simone Carnemolla, Salvatore Calcagno, Daniela Giordano +2
Contrastively trained vision-language models such as CLIP provide strong zero-shot transfer by aligning images and text in a shared embedding space. However, adapting these models…
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…
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…