  Visualizing the features captured by Convolutional Neural Networks (CNNs) is
one of the conventional approaches to interpret the predictions made by these
models in numerous image recognition applications. Grad-CAM is a popular
solution that provides such a visualization by combining the activation maps
obtained from the model. However, the average gradient-based terms deployed in
this method underestimates the contribution of the representations discovered
by the model to its predictions. Addressing this problem, we introduce a
solution to tackle this issue by computing the path integral of the
gradient-based terms in Grad-CAM. We conduct a thorough analysis to demonstrate
the improvement achieved by our method in measuring the importance of the
extracted representations for the CNN's predictions, which yields to our
method's administration in object localization and model interpretation.
