EXplainable AI (XAI) is an essential topic to improve human understanding of
deep neural networks (DNNs) given their black-box internals. For computer
vision tasks, mainstream pixel-based XAI methods explain DNN decisions by
identifying important pixels, and emerging concept-based XAI explore forming
explanations with concepts (e.g., a head in an image). However, pixels are
generally hard to interpret and sensitive to the imprecision of XAI methods,
whereas "concepts" in prior works require human annotation or are limited to
pre-defined concept sets. On the other hand, driven by large-scale
pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful
and promotable framework for performing precise and comprehensive instance
segmentation, enabling automatic preparation of concept sets from a given
image. This paper for the first time explores using SAM to augment
concept-based XAI. We offer an effective and flexible concept-based explanation
method, namely Explain Any Concept (EAC), which explains DNN decisions with any
concept. While SAM is highly effective and offers an "out-of-the-box" instance
segmentation, it is costly when being integrated into defacto XAI pipelines. We
thus propose a lightweight per-input equivalent (PIE) scheme, enabling
efficient explanation with a surrogate model. Our evaluation over two popular
datasets (ImageNet and COCO) illustrate the highly encouraging performance of
EAC over commonly-used XAI methods.

本研究探索使用具備精確及全面物件分割能力的 Segment Anything Model (SAM) 來增強基於概念的可解釋人工智慧 (Explainable AI, XAI) 方法。我們提出了一種稱為 Explain Any Concept (EAC) 的概念解釋方法，可以有效靈活地解釋深度神經網絡 (DNN) 的決策。我們的評估結果表明，EAC 方法在 ImageNet 和 COCO 兩個常用的數據集上的表現非常鼓舞人心，優於常用的 XAI 方法。