  This paper investigates modifying an existing neural network architecture for
static saliency prediction using two types of recurrences that integrate
information from the temporal domain. The first modification is the addition of
a ConvLSTM within the architecture, while the second is a conceptually simple
exponential moving average of an internal convolutional state. We use weights
pre-trained on the SALICON dataset and fine-tune our model on DHF1K. Our
results show that both modifications achieve state-of-the-art results and
produce similar saliency maps. Source code is available at
https://git.io/fjPiB.
