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.

在可解释人工智能（XAI）领域中，解释深度神经网络（DNN）在计算机视觉任务中的决策是一个重要过程。本文通过在原代理模型中引入附加的非线性层，提出了一种改进的 EAC 模型，以提高其性能。在 ImageNet 和 MS COCO 数据集中，与原 EAC 模型相比，所提出的方法表现出明显改善。