The field of Explainable Artificial Intelligence (XAI) aims to improve the interpretability of black-box machine learning models. Building a heatmap based on the importance value of input features is a popular method for explaining the underlying functions of such models in producing their predictions. Heatmaps are almost understandable to humans, yet they are not without flaws. Non-expert users, for example, may not fully understand the logic of heatmaps (the logic in which relevant pixels to the model's prediction are highlighted with different intensities or colors). Additionally, objects and regions of the input image that are relevant to the model prediction are frequently not entirely differentiated by heatmaps. In this paper, we propose a framework called TbExplain that employs XAI techniques and a pre-trained object detector to present text-based explanations of scene classification models. Moreover, TbExplain incorporates a novel method to correct predictions and textually explain them based on the statistics of objects in the input image when the initial prediction is unreliable. To assess the trustworthiness and validity of the text-based explanations, we conducted a qualitative experiment, and the findings indicated that these explanations are sufficiently reliable. Furthermore, our quantitative and qualitative experiments on TbExplain with scene classification datasets reveal an improvement in classification accuracy over ResNet variants.

翻译：可解释人工智能（XAI）领域旨在提升黑箱机器学习模型的可解释性。基于输入特征重要性值构建热力图是解释此类模型预测机制的主流方法。热力图虽基本可被人类理解，但仍存在缺陷。例如，非专业用户可能无法完全理解热力图的逻辑（即通过不同强度或颜色高亮显示与模型预测相关的像素的逻辑）。此外，热力图常无法完全区分与模型预测相关的输入图像中的物体与区域。本文提出名为TbExplain的框架，该框架利用XAI技术与预训练目标检测器，为场景分类模型提供基于文本的解释。当初始预测不可靠时，TbExplain还创新性地引入基于输入图像中物体统计特征的方法，对预测结果进行校正并生成文本化解释。为评估文本解释的可信度与有效性，我们开展了定性实验，结果表明这些解释具有充分可靠性。此外，在场景分类数据集上对TbExplain进行的定量与定性实验显示，其分类准确率相较ResNet变体有所提升。