  Here we present DeepGaze II, a model that predicts where people look in
images. The model uses the features from the VGG-19 deep neural network trained
to identify objects in images. Contrary to other saliency models that use deep
features, here we use the VGG features for saliency prediction with no
additional fine-tuning (rather, a few readout layers are trained on top of the
VGG features to predict saliency). The model is therefore a strong test of
transfer learning. After conservative cross-validation, DeepGaze II explains
about 87% of the explainable information gain in the patterns of fixations and
achieves top performance in area under the curve metrics on the MIT300 hold-out
benchmark. These results corroborate the finding from DeepGaze I (which
explained 56% of the explainable information gain), that deep features trained
on object recognition provide a versatile feature space for performing related
visual tasks. We explore the factors that contribute to this success and
present several informative image examples. A web service is available to
compute model predictions at http://deepgaze.bethgelab.org.
