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Here we present DeepGaze II, a model that predicts where people look inimages. The model uses the features from the VGG-19 deep neural network trainedto identify objects in images. Contrary to other saliency models that use deepfeatures, here we use the VGG features for saliency prediction with noadditional fine-tuning (rather, a few readout layers are trained on top of theVGG features to predict saliency). The model is therefore a strong test oftransfer learning. After conservative cross-validation, DeepGaze II explainsabout 87% of the explainable information gain in the patterns of fixations andachieves top performance in area under the curve metrics on the MIT300 hold-outbenchmark. These results corroborate the finding from DeepGaze I (whichexplained 56% of the explainable information gain), that deep features trainedon object recognition provide a versatile feature space for performing relatedvisual tasks. We explore the factors that contribute to this success andpresent several informative image examples. A web service is available tocompute model predictions at http://deepgaze.bethgelab.org.

DeepGaze II: Reading fixations from deep features trained on object recognition