  In this paper we present the design and evaluation of an end-to-end
trainable, deep neural network with a visual attention mechanism for
memorability estimation in still images. We analyze the suitability of transfer
learning of deep models from image classification to the memorability task.
Further on we study the impact of the attention mechanism on the memorability
estimation and evaluate our network on the SUN Memorability and the LaMem
datasets. Our network outperforms the existing state of the art models on both
datasets in terms of the Spearman's rank correlation as well as the mean
squared error, closely matching human consistency.
