  Large pre-trained Vision-Language Models (VLMs) have become ubiquitous
foundational components of other models and downstream tasks. Although
powerful, our empirical results reveal that such models might not be able to
identify fine-grained concepts. Specifically, the explanations of VLMs with
respect to fine-grained concepts are entangled and mislocalized. To address
this issue, we propose to DisEntAngle and Localize (DEAL) the concept-level
explanations for VLMs without human annotations. The key idea is encouraging
the concept-level explanations to be distinct while maintaining consistency
with category-level explanations. We conduct extensive experiments and ablation
studies on a wide range of benchmark datasets and vision-language models. Our
empirical results demonstrate that the proposed method significantly improves
the concept-level explanations of the model in terms of disentanglability and
localizability. Surprisingly, the improved explainability alleviates the
model's reliance on spurious correlations, which further benefits the
prediction accuracy.
