fixated regions and head motion. It is built based on two assumptions. Firstly, a person’s gaze tends to be located on the same object during each fixation, and a large gaze shift almost always occurs along with large head motion [@land2004coordination]. Secondly, patterns in the temporal shift between regions of attention are dependent on the performed task and can be learned from data. The last module is based on a fully convolutional network which fuses the saliency map and the attention map from the first two modules and generates a final gaze map, from which the final prediction of 2D gaze position is made.

Main contributions of this work are summarized as follows:

-   We propose a new hybrid model for gaze prediction that leverages both bottom-up visual saliency and task-dependent attention transition.

-   We propose a novel model for task-dependent attention transition that explores the patterns in the temporal shift of gaze fixations and can be used to predict the region of attention based on previous fixations.

-   The proposed approach achieves state-of-the-art gaze prediction performance on public egocentric activity datasets.

Related Works
=============

### Visual Saliency Prediction.

Visual saliency is a way to measure image regions that are 