cues and may not generalize well to other activities where hands are not always involved. Recently, Zhang *et al.* [@zhang2017deep] proposed the gaze anticipation problem in egocentric videos. In their work, a Generative Adversarial Network (GAN) based model is proposed to generate future frames from a current video frame, and gaze positions are predicted on the generated future frames based on a 3D-CNN based saliency prediction model.

In this paper, we propose a new hybrid model to predict gaze in egocentric videos, which combines bottom-up visual saliency with task-dependent attention transition. To the best of our knowledge, this is the first work to explore the patterns in attention transition for egocentric gaze prediction.

Gaze Prediction Model
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In this section, we first give overview of the network architecture of the proposed gaze prediction model, and then explain the details of each component. The details of training the model are provided in the end.

![The architecture of our proposed gaze prediction model. The red crosses in the figure indicate ground truth gaze positions.[]{data-label="fig:wholemodel"}](template.pdf){width="\linewidth"}

Model Architecture
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Given consecutive video frames as input, we aim to predict a gaze position in each frame. To leverage both bottom-up visual saliency and task-dependent attention 