4 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
MorphoSHAP: Rethinking the Unit of Attribution in Explanation for Deep Visual Models
Anirudh Prabhakaran, Alexandre Rocchi, Gianni Franchi
Visual attribution methods typically explain predictions using pixels, superpixels, or regular patches. These representations can localize important regions, but provide limited in…
Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier +2
This paper addresses explainable AI (XAI) through the lens of Concept Bottleneck Models (CBMs) that do not require explicit concept annotations, relying instead on concepts extract…
Explainability for Vision Foundation Models: A Survey
Rémi Kazmierczak, Eloïse Berthier, Goran Frehse +1
As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven b…
Benchmarking XAI Explanations with Human-Aligned Evaluations
Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier +9
We introduce PASTA (Perceptual Assessment System for explanaTion of Artificial Intelligence), a novel human-centric framework for evaluating eXplainable AI (XAI) techniques in comp…
CLIP-QDA: An Explainable Concept Bottleneck Model
Rémi Kazmierczak, Eloïse Berthier, Goran Frehse +1
In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification. Drawing inspiration from…