  This work adapts a deep neural model for image saliency prediction to the
temporal domain of egocentric video. We compute the saliency map for each video
frame, firstly with an off-the-shelf model trained from static images, secondly
by adding a a convolutional or conv-LSTM layers trained with a dataset for
video saliency prediction. We study each configuration on EgoMon, a new dataset
made of seven egocentric videos recorded by three subjects in both free-viewing
and task-driven set ups. Our results indicate that the temporal adaptation is
beneficial when the viewer is not moving and observing the scene from a narrow
field of view. Encouraged by this observation, we compute and publish the
saliency maps for the EPIC Kitchens dataset, in which viewers are cooking.
Source code and models available at
https://imatge-upc.github.io/saliency-2018-videosalgan/
