  Nearly all existing visual saliency models by far have focused on predicting
a universal saliency map across all observers. Yet psychology studies suggest
that visual attention of different observers can vary significantly under
specific circumstances, especially a scene is composed of multiple salient
objects. To study such heterogenous visual attention pattern across observers,
we first construct a personalized saliency dataset and explore correlations
between visual attention, personal preferences, and image contents.
Specifically, we propose to decompose a personalized saliency map (referred to
as PSM) into a universal saliency map (referred to as USM) predictable by
existing saliency detection models and a new discrepancy map across users that
characterizes personalized saliency. We then present two solutions towards
predicting such discrepancy maps, i.e., a multi-task convolutional neural
network (CNN) framework and an extended CNN with Person-specific Information
Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the
effectiveness of our models for PSM prediction as well their generalization
capability for unseen observers.
