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Personalization of Saliency Estimation
Most existing saliency models use low-level features or task descriptions
when generating attention predictions. However, the link between observer
characteristics and gaze patterns is rarely investigated. We present a novel
saliency prediction technique which takes viewers' identities and personal
traits into consideration when modeling human attention. Instead of only
computing image salience for average observers, we consider the interpersonal
variation in the viewing behaviors of observers with different personal traits
and backgrounds. We present an enriched derivative of the GAN network, which is
able to generate personalized saliency predictions when fed with image stimuli
and specific information about the observer. Our model contains a generator
which generates grayscale saliency heat maps based on the image and an observer
label. The generator is paired with an adversarial discriminator which learns
to distinguish generated salience from ground truth salience. The discriminator
also has the observer label as an input, which contributes to the
personalization ability of our approach. We evaluate the performance of our
personalized salience model by comparison with a benchmark model along with
other un-personalized predictions, and illustrate improvements in prediction
accuracy for all tested observer groups
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