Saliency prediction is a well studied problem in
computer vision. Early saliency models were based on low-level
hand-crafted feature derived from insights gained in neuroscience
and psychophysics. In the wake of deep learning breakthrough,
a new cohort of models were proposed based on neural network
architectures, allowing significantly higher gaze prediction than
previous shallow models, on all metrics. However, most models
treat the saliency prediction as a regression problem, and accurate
regression of high-dimensional data is known to be a hard
problem. Furthermore, it is unclear that intermediate levels of
saliency (ie, neither very high, nor very low) are meaningful:
Something is either salient, or it is not. Drawing from those two
observations, we reformulate the saliency prediction problem as
a salient region segmentation problem. We demonstrate that the
reformulation allows for faster convergence than the classical
regression problem, while performance is comparable to stateof-the-art. We also visualise the general features learned by the
model, which are showed to be consistent with insights from
psychophysics.Engineering and Physical Sciences Research Council (EPSRC