  One of the most fundamental and information-laden actions humans do is to
look at objects. However, a survey of current works reveals that existing
gaze-related datasets annotate only the pixel being looked at, and not the
boundaries of a specific object of interest. This lack of object annotation
presents an opportunity for further advancing gaze estimation research. To this
end, we present a challenging new task called gaze object prediction, where the
goal is to predict a bounding box for a person's gazed-at object. To train and
evaluate gaze networks on this task, we present the Gaze On Objects (GOO)
dataset. GOO is composed of a large set of synthetic images (GOO Synth)
supplemented by a smaller subset of real images (GOO-Real) of people looking at
objects in a retail environment. Our work establishes extensive baselines on
GOO by re-implementing and evaluating selected state-of-the art models on the
task of gaze following and domain adaptation. Code is available on github.
