Explainable artificial intelligence (XAI) has witnessed significant advances in the field of object recognition, with saliency maps being used to highlight image features relevant to the predictions of learned models. Although these advances have made AI-based technology more interpretable to humans, several issues have come to light. Some approaches present explanations irrelevant to predictions, and cannot guarantee the validity of XAI (axioms). In this study, we propose the Baseline Shapley-based Explainable Detector (BSED), which extends the Shapley value to object detection, thereby enhancing the validity of interpretation. The Shapley value can attribute the prediction of a learned model to a baseline feature while satisfying the explainability axioms. The processing cost for the BSED is within the reasonable range, while the original Shapley value is prohibitively computationally expensive. Furthermore, BSED is a generalizable method that can be applied to various detectors in a model-agnostic manner, and interpret various detection targets without fine-grained parameter tuning. These strengths can enable the practical applicability of XAI. We present quantitative and qualitative comparisons with existing methods to demonstrate the superior performance of our method in terms of explanation validity. Moreover, we present some applications, such as correcting detection based on explanations from our method.

翻译：可解释人工智能（XAI）在目标识别领域取得了显著进展，其中显著性图被用于突出与学习模型预测相关的图像特征。尽管这些进展使基于人工智能的技术对人类更具可解释性，但也暴露出一些问题：部分方法提供的解释与预测无关，且无法保证XAI的公理有效性。本研究提出基于基线沙普利值的可解释检测器（BSED），将沙普利值扩展至目标检测领域，从而提升解释的有效性。沙普利值在满足可解释性公理的前提下，能将学习模型的预测归因于基线特征。BSED的处理成本处于合理范围内，而原始沙普利值的计算代价极为高昂。此外，BSED是一种可泛化的方法，能以模型无关的方式应用于各类检测器，且无需精细化参数调优即可解释多种检测目标。这些优势可增强XAI的实际应用价值。我们通过与现有方法的定量与定性对比，证明了本方法在解释有效性方面的优越性能。同时，我们还展示了基于本方法解释修正检测结果等应用案例。