1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CV2026★ 1 cited
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
Antonio De Santis, Riccardo Campi, Matteo Bianchi +1
Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexit…
cs.CV2026
A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability
Giacomo Astolfi, Matteo Bianchi, Riccardo Campi +2
Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or object parts) by linking inter…