Deep Learning algorithms have recently gained significant attention due to
their impressive performance. However, their high complexity and
un-interpretable mode of operation hinders their confident deployment in
real-world safety-critical tasks. This work targets ante hoc interpretability,
and specifically Concept Bottleneck Models (CBMs). Our goal is to design a
framework that admits a highly interpretable decision making process with
respect to human understandable concepts, on multiple levels of granularity. To
this end, we propose a novel hierarchical concept discovery formulation
leveraging: (i) recent advances in image-text models, and (ii) an innovative
formulation for multi-level concept selection via data-driven and sparsity
inducing Bayesian arguments. Within this framework, concept information does
not solely rely on the similarity between the whole image and general
unstructured concepts; instead, we introduce the notion of concept hierarchy to
uncover and exploit more granular concept information residing in
patch-specific regions of the image scene. As we experimentally show, the
proposed construction not only outperforms recent CBM approaches, but also
yields a principled framework towards interpetability.

利用深度学习算法设计了一种多层次概念发现框架，允许人类理解的概念在决策过程中高度解释性，通过图像 - 文本模型和基于数据驱动和稀疏的贝叶斯算法进行多层次概念选择，实验结果表明这种构建不仅胜过最近的 CBM 方法，而且为解释性提供了一个有原则的框架。