  Human visual attention is subjective and biased according to the personal
preference of the viewer, however, current works of saliency detection are
general and objective, without counting the factor of the observer. This will
make the attention prediction for a particular person not accurate enough. In
this work, we present the novel idea of personalized attention prediction and
develop Personalized Attention Network (PANet), a convolutional network that
predicts saliency in images with personal preference. The model consists of two
streams which share common feature extraction layers, and one stream is
responsible for saliency prediction, while the other is adapted from the
detection model and used to fit user preference. We automatically collect user
preference from their albums and leaves them freedom to define what and how
many categories their preferences are divided into. To train PANet, we
dynamically generate ground truth saliency maps upon existing detection labels
and saliency labels, and the generation parameters are based upon our collected
datasets consists of 1k images. We evaluate the model with saliency prediction
metrics and test the trained model on different preference vectors. The results
have shown that our system is much better than general models in personalized
saliency prediction and is efficient to use for different preferences.
