Explainability is an aspect of modern AI that is vital for impact and usability in the real world. The main objective of this paper is to emphasise the need to understand the predictions of Computer Vision models, specifically Convolutional Neural Network (CNN) based models. Existing methods of explaining CNN predictions are mostly based on Gradient-weighted Class Activation Maps (Grad-CAM) and solely focus on a single target class. We show that from the point of the target class selection, we make an assumption on the prediction process, hence neglecting a large portion of the predictor CNN model's thinking process. In this paper, we present an exhaustive methodology called Fused Multi-class Gradient-weighted Class Activation Map (FM-G-CAM) that considers multiple top predicted classes, which provides a holistic explanation of the predictor CNN's thinking rationale. We also provide a detailed and comprehensive mathematical and algorithmic description of our method. Furthermore, along with a concise comparison of existing methods, we compare FM-G-CAM with Grad-CAM, highlighting its benefits through real-world practical use cases. Finally, we present an open-source Python library with FM-G-CAM implementation to conveniently generate saliency maps for CNN-based model predictions.

本文介绍了一种全面的解释性方法FM-G-CAM，它考虑了多个顶级预测类别，并提供了对预测卷积神经网络思维过程的完整解释，同时与Grad-CAM进行了比较，并通过实际应用案例突出了其优点。最后，我们介绍了一个使用FM-G-CAM生成卷积神经网络模型预测的显著性图的开源Python库。

FM-G-CAM: 计算机视觉中可解释AI的整体方法