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.

翻译：可解释人工智能（XAI）是提升人类对深度神经网络（DNN）理解的关键课题，因其内部机制如同黑箱。针对计算机视觉任务，主流基于像素的XAI方法通过识别重要像素来解释DNN决策，而新兴的基于概念的XAI则探索利用概念（例如图像中的头部）进行解释。然而，像素通常难以解释且容易受XAI方法精度不足的影响，而先前工作中的“概念”则需要人工标注或局限于预定义概念集。另一方面，在大规模预训练的驱动下，Segment Anything Model（SAM）已被证明是一种强大且可推广的框架，能够对图像进行精确而全面的实例分割，从而自动为给定图像准备概念集。本文首次探索利用SAM增强基于概念的XAI。我们提出了一种有效且灵活的基于概念的解释方法，即解释任何概念（EAC），该方法能够用任意概念解释DNN决策。尽管SAM非常有效且提供了“开箱即用”的实例分割能力，但将其集成到标准的XAI流程中成本较高。因此，我们提出了一种轻量级的逐输入等效（PIE）方案，能够通过替代模型实现高效解释。我们在两个流行数据集（ImageNet和COCO）上的评估表明，EAC相比常用的XAI方法具有非常令人鼓舞的性能。