  Recent results suggest that state-of-the-art saliency models perform far from
optimal in predicting fixations. This lack in performance has been attributed
to an inability to model the influence of high-level image features such as
objects. Recent seminal advances in applying deep neural networks to tasks like
object recognition suggests that they are able to capture this kind of
structure. However, the enormous amount of training data necessary to train
these networks makes them difficult to apply directly to saliency prediction.
We present a novel way of reusing existing neural networks that have been
pretrained on the task of object recognition in models of fixation prediction.
Using the well-known network of Krizhevsky et al. (2012), we come up with a new
saliency model that significantly outperforms all state-of-the-art models on
the MIT Saliency Benchmark. We show that the structure of this network allows
new insights in the psychophysics of fixation selection and potentially their
neural implementation. To train our network, we build on recent work on the
modeling of saliency as point processes.
