frames selected from the sampled frames of GTEA Gaze Plus dataset. Each selected frame is not only under a gaze fixation, but also contains the object (or region) that is to be attended at the next fixation. We manually draw bounding boxes on those regions by inspecting future frames. The dataset is used to examine whether or not our model trained on GTEA Gaze Plus (excluding GTEA-sub) has successfully learned the task-dependent attention transition.

Evaluation Metrics
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We use two standard evaluation metrics for gaze prediction in egocentric videos: Area Under the Curve (AUC) [@AUC] and Average Angular Error (AAE) [@AAE]. **AUC** is the area under a curve of true positive rate versus false positive rate for different thresholds on the predicted gaze map. It is a commonly used evaluation metric in saliency prediction. **AAE** is the average angular distance between the predicted and the ground truth gaze positions.

Results on Gaze Prediction
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### Baselines.

We use the following baselines for gaze prediction:

-   *Saliency prediction algorithms*: We compare our method with several representative saliency prediction methods. More specifically, we used Itti’s model [@itti2000saliency], Graph Based Visual Saliency (GBVS [@harel2007graph]), and a deep neural network based saliency model as 