videos captured from a first-person perspective [@kitani2011fast][@cai2015scalable][@cai2017ego], has become an emerging field in computer vision. In particular, as the camera wearer’s point-of-gaze in egocentric video contains important information about interacted objects and the camera wearer’s intent [@huang2017temporal], gaze prediction can be used to infer important regions in images and videos to reduce the amount of computation needed in learning and inference of various analysis tasks [@fathi2012learning][@xu2015gaze][@cai2016understanding][@cai2018desktop].

This paper aims to develop a computational model for predicting the camera wearer’s point-of-gaze from an egocentric video. Most previous methods have formulated gaze prediction as the problem of saliency detection, and computational models of visual saliency have been studied to the find image regions that are likely to attract human attention. The saliency-based paradigm is reasonable because it is known that highly salient regions are strongly correlated with actual gaze locations [@parkhurst2002modeling]. However, the saliency model-based gaze prediction becomes much more difficult in natural dynamic scenes, *e.g.* cooking in a kitchen, where high-level knowledge of the task has a strong influence on human attention.

In a natural dynamic scene, a person perceives the surrounding environment with a series of gaze fixations which point to the objects/regions related to the person’s interactions with the 