Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising
field of research, since explanations that refer to concepts (ie, semantically meaningful parts …

Deep-learning models can extract a rich assortment of features from data. Which features a
model uses depends not only on predictivity-how reliably a feature indicates train-set labels …

This paper studies the problem of concept-based interpretability of transformer
representations for videos. Concretely we seek to explain the decision-making process of …

CLIP embeddings have demonstrated remarkable performance across a wide range of
computer vision tasks. However, these high-dimensional, dense vector representations are …

Research on Explainable Artificial Intelligence has recently started exploring the idea of
producing explanations that, rather than being expressed in terms of low-level features, are …

To solve ever more complex problems, Deep Neural Networks are scaled to billions of
parameters, leading to huge computational costs. An effective approach to reduce …