In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply understands what's going on inside these models. Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time.

翻译：近年来，人工智能日益广泛应用于众多不同领域，并对人类生活产生深远而直接的影响。随之而来的是对模型预测原理的理解需求。由于当前大多数高精度模型都是黑箱模型，无论是人工智能科学家还是最终用户，都无法深入了解这些模型的内部运作机制。因此，许多算法被研究用于解释人工智能模型，尤其是计算机视觉领域中图像分类问题相关的模型，例如LIME、CAM、GradCAM。然而，这些算法仍存在局限性，如LIME执行时间长，CAM在具体性和清晰性方面的解释令人困惑。因此，本文提出一种名为分割-类别激活映射（SeCAM）的新方法，该方法结合了上述算法的优势，同时克服了它们的缺点。我们使用ImageNet大规模视觉识别挑战赛（ILSVRC）数据集中的多种模型（包括ResNet50、Inception-v3、VGG16）对该算法进行了测试。结果表明，该算法在极短的时间内满足了具体解释的所有要求，效果显著。