  This paper addresses the challenging problem of estimating the general visual
attention of people in images. Our proposed method is designed to work across
multiple naturalistic social scenarios and provides a full picture of the
subject's attention and gaze. In contrast, earlier works on gaze and attention
estimation have focused on constrained problems in more specific contexts. In
particular, our model explicitly represents the gaze direction and handles
out-of-frame gaze targets. We leverage three different datasets using a
multi-task learning approach. We evaluate our method on widely used benchmarks
for single-tasks such as gaze angle estimation and attention-within-an-image,
as well as on the new challenging task of generalized visual attention
prediction. In addition, we have created extended annotations for the MMDB and
GazeFollow datasets which are used in our experiments, which we will publicly
release.
