most citedRevisiting the robustness of post-hoc interpretability methods

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cs.CV2025

Tell me why: Visual foundation models as self-explainable classifiers

Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1

Visual foundation models (VFMs) have become increasingly popular due to their state-of-the-art performance. However, interpretability remains crucial for critical applications. In…

cs.CV2024

ProtoS-ViT: Visual foundation models for sparse self-explainable classifications

Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1

Prototypical networks aim to build intrinsically explainable models based on the linear summation of concepts. Concepts are coherent entities that we, as humans, can recognize and…