  In the evolving field of Explainable AI (XAI), interpreting the decisions of
deep neural networks (DNNs) in computer vision tasks is an important process.
While pixel-based XAI methods focus on identifying significant pixels, existing
concept-based XAI methods use pre-defined or human-annotated concepts. The
recently proposed Segment Anything Model (SAM) achieved a significant step
forward to prepare automatic concept sets via comprehensive instance
segmentation. Building upon this, the Explain Any Concept (EAC) model emerged
as a flexible method for explaining DNN decisions. EAC model is based on using
a surrogate model which has one trainable linear layer to simulate the target
model. In this paper, by introducing an additional nonlinear layer to the
original surrogate model, we show that we can improve the performance of the
EAC model. We compare our proposed approach to the original EAC model and
report improvements obtained on both ImageNet and MS COCO datasets.
