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arXiv · 1804.03115

AMNet: Memorability Estimation with Attention

Jiri Fajtl·Vasileios Argyriou·Dorothy Monekosso·Paolo Remagnino
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Abstract

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.

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Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino. 2018-04-09. AMNet: Memorability Estimation with Attention. https://arxiv.org/abs/1804.03115

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