  Recent research in deep learning methodology has led to a variety of complex
modelling techniques in computer vision (CV) that reach or even outperform
human performance. Although these black-box deep learning models have obtained
astounding results, they are limited in their interpretability and transparency
which are critical to take learning machines to the next step to include them
in sensitive decision-support systems involving human supervision. Hence, the
development of explainable techniques for computer vision (XCV) has recently
attracted increasing attention. In the realm of XCV, Class Activation Maps
(CAMs) have become widely recognized and utilized for enhancing
interpretability and insights into the decision-making process of deep learning
models. This work presents a comprehensive overview of the evolution of Class
Activation Map methods over time. It also explores the metrics used for
evaluating CAMs and introduces auxiliary techniques to improve the saliency of
these methods. The overview concludes by proposing potential avenues for future
research in this evolving field.
