Introduction of Explicit Visual Saliency in Training of Deep CNNs: Application to Architectural Styles Classification

Introduction of visual saliency or interestingness in the content selection for image classification tasks is an intensively researched topic. It has been namely fulfilled for feature selection in feature-based methods. Nowadays, in the winner classifiers of visual content such as Deep Convolutional Neural Networks, visual saliency maps have not been introduced explicitly. Pooling features in CNNs is known as a good strategy to reduce data dimensionality, computational complexity and summarize representative features for subsequent layers. In this paper we introduce visual saliency in network pooling layers to spatially filter relevant features for deeper layers. Our experiments are conducted in a specific task to identify Mexican architectural styles. The results are promising: proposed approach reduces model loss and training time keeping the same accuracy as the base-line CNN.