likely to attract human attention by weighting each channel of the feature map differently. We train a Long Short Term Memory (LSTM) model [@lstm] to predict a vector of channel weights which is used to predict the region of attention at next fixation. Figure \[fig:lstm\] depicts the framework of the proposed attention transition module. The module is composed of a channel weight extractor (C), a fixation state predictor (P), and a LSTM-based weight predictor (L).

![The architecture of the attention transition module.[]{data-label="fig:lstm"}](template2.pdf){width="\linewidth"}

The channel weight extractor takes as input the latent representation $F^S_{t-1}$ and the predicted gaze point $g_{t-1}$ from the previous frame. $F^S_{t-1}$ is in fact a stack of feature maps with spatial resolution $14\times14$ and 512 channels. From each channel, we project the predicted gaze position $g_{t-1}$ onto the 14$\times$14 feature map, and crop a fixed size area with height $H_c$ and width $W_c$ centered at the projected gaze position. We then average the value of the cropped feature map at each channel, obtaining a 512-dimensional vector of channel weight $w_{t-1}$: $$w_{t-1} = C(F^S_{t-1}, g_{t-1})$$ where $C(\cdot)$ indicates the cropping and averaging operation, $w_{t-1}$ is used as feature representation of the region of attention around the gaze point 