We compare the proposed model with other state-of-the-art methods and provide detailed analysis of our model through ablation study and visualization of outputs of different modules. Furthermore, to examine our model’s ability in learning attention transition, we visualize output of the attention transition module on a newly collected test set from GTEA Gaze Plus dataset (denoted as **GTEA-sub**).

Datasets
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We introduce the datasets used for gaze prediction and attention transition.

**GTEA Gaze** contains 17 video sequences of kitchen tasks performed by 14 subjects. Each video clip lasts for about 4 minutes with the frame rate of 15 fps and an image resolution of 480 $\times$ 640. We use videos 1, 4, 6-22 as a training set and the rest as a test set as in Yin *et al.* [@li2013learning]. **GTEA Gaze Plus** contains 37 videos with the frame rate of 24 fps and an image resolution of 960 $\times$ 1280. In this dataset each of the 5 subjects performs 7 meal preparation activities in a more natural environment. Each video clip is 10 to 15 minute long on average. Similarly to [@li2013learning], gaze prediction accuracy is evaluated with 5-fold cross validation across all 5 subjects.

**GTEA-sub**\[gtea-sub\] contains 227 video 