  By and large, existing computational models of visual attention tacitly
assume perfect vision and full access to the stimulus and thereby deviate from
foveated biological vision. Moreover, modeling top-down attention is generally
reduced to the integration of semantic features without incorporating the
signal of a high-level visual tasks that have been shown to partially guide
human attention. We propose the Neural Visual Attention (NeVA) algorithm to
generate visual scanpaths in a top-down manner. With our method, we explore the
ability of neural networks on which we impose a biologically-inspired foveated
vision constraint to generate human-like scanpaths without directly training
for this objective. The loss of a neural network performing a downstream visual
task (i.e., classification or reconstruction) flexibly provides top-down
guidance to the scanpath. Extensive experiments show that our method
outperforms state-of-the-art unsupervised human attention models in terms of
similarity to human scanpaths. Additionally, the flexibility of the framework
allows to quantitatively investigate the role of different tasks in the
generated visual behaviors. Finally, we demonstrate the superiority of the
approach in a novel experiment that investigates the utility of scanpaths in
real-world applications, where imperfect viewing conditions are given.
