visual saliency map with each pixel value within the range of $[0,1]$. Details of the architecture of the decoder is described in Section \[details\]. The equation for generating the visual saliency map is: $$\label{equa:sp}
    G^s_t=S(F^S_t,F^T_t)$$

However, a saliency map alone does not predict accurately where people actually look [@yamada2010can], especially in egocentric videos of natural dynamic scenes where the knowledge of a task has a strong influence on human gaze. To achieve better gaze prediction, high-level knowledge about a task, such as which object is to be looked at and manipulated next, has to be considered.

Attention Transition Module
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During the procedure of performing a task, the task knowledge strongly influences the temporal transition of human gaze fixations on a series of objects. Therefore, given previous gaze fixations, it is possible to anticipate the image region where next attention occurs. However, direct modeling the object transition explicitly such as using object categories is problematic since a reliable and generic object detector is needed. Motivated by the fact that different channels of a feature map in top convolutional layers correspond well to spatial responses of different high-level semantics such as different object categories [@chen2016sca][@zhou2016learning], we represent the region that is 