Explainable event recognition

The literature shows outstanding capabilities for Convolutional Neural Networks (CNNs) in event recognition in images. However, fewer attempts are made to analyze the potential causes behind the decisions of the models and explore whether the predictions are based on event-salient objects/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 four 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 crowd-sourcing 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 on event-related objects/regions.