at frame $t-1$.

The fixation state predictor takes the latent representation of $F^T_{t-1}$ as input and outputs a probabilistic score of fixation state $f^p_{t-1}=P(F^T_{t-1}) \in [0,1]$. Basically, the score tells how likely fixation is occurring in the frame $t-1$. The fixation state predictor is composed by three fully connected layers followed by a final softmax layer to output a probabilistic score for gaze fixation state.

We use a LSTM to learn the attention transition by learning the transition of channel weights. The LSTM is trained based on a sequence of channel weight vectors extracted from images *at the boundaries of* all gaze fixation periods with ground-truth gaze points, *i.e.* we only extract one channel weight vector for each fixation to learn its transition between fixations. During testing, given a channel weight vector $w_{t-1}$, the trained LSTM outputs a channel weight vector $L(w_{t-1})$ that represents the region of attention at next gaze fixation. We also consider the dynamic behavior of gaze and its influence on attention transition. Intuitively speaking, during a period of fixation, the region of attention tends to remain unchanged, and the attended region changes only when saccade happens. Therefore, we compute the region of attention at current frame 