  The literature shows outstanding capabilities for CNNs in event recognition
in images. However, fewer attempts are made to analyze the potential causes
behind the decisions of the models and exploring whether the predictions are
based on event-salient objects or regions? To explore this important aspect of
event recognition, in this work, we propose an explainable event recognition
framework relying on Grad-CAM and an Xception architecture-based CNN model.
Experiments are conducted on three large-scale datasets covering a diversified
set of natural disasters, social, and sports events. Overall, the model showed
outstanding generalization capabilities obtaining overall F1-scores of 0.91,
0.94, and 0.97 on natural disasters, social, and sports events, respectively.
Moreover, for subjective analysis of activation maps generated through Grad-CAM
for the predicted samples of the model, a crowdsourcing study is conducted to
analyze whether the model's predictions are based on event-related
objects/regions or not? The results of the study indicate that 78%, 84%, and
78% of the model decisions on natural disasters, sports, and social events
datasets, respectively, are based onevent-related objects or regions.
