  Egocentric vision consists in acquiring images along the day from a first
person point-of-view using wearable cameras. The automatic analysis of this
information allows to discover daily patterns for improving the quality of life
of the user. A natural topic that arises in egocentric vision is storytelling,
that is, how to understand and tell the story relying behind the pictures. In
this paper, we tackle storytelling as an egocentric sequences description
problem. We propose a novel methodology that exploits information from
temporally neighboring events, matching precisely the nature of egocentric
sequences. Furthermore, we present a new method for multimodal data fusion
consisting on a multi-input attention recurrent network. We also publish the
first dataset for egocentric image sequences description, consisting of 1,339
events with 3,991 descriptions, from 55 days acquired by 11 people.
Furthermore, we prove that our proposal outperforms classical attentional
encoder-decoder methods for video description.
