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Predicting attention is a popular topic at the intersection of human andcomputer vision. However, even though most of the available video saliency datasets and models claim to target human observers' fixations, they fail todifferentiate them from smooth pursuit (SP), a major eye movement type that isunique to perception of dynamic scenes. In this work, we highlight theimportance of SP and its prediction (which we call supersaliency, due togreater selectivity compared to fixations), and aim to make its distinctionfrom fixations explicit for computational models. To this end, we (i) usealgorithmic and manual annotations of SP and fixations for two well-establishedvideo saliency data sets, (ii) train Slicing Convolutional Neural Networks forsaliency prediction on either fixation- or SP-salient locations, and (iii)evaluate our and 26 publicly available dynamic saliency models on three datasets against traditional saliency and supersaliency ground truth. Overall, ourmodels outperform the state of the art in both the new supersaliency and thetraditional saliency problem settings, for which literature models areoptimized. Importantly, on two independent data sets, our supersaliency modelshows greater generalization ability and outperforms all other models, even forfixation prediction.

Supersaliency: A Novel Pipeline for Predicting Smooth Pursuit-Based Attention Improves Generalizability of Video Saliency