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This paper addresses the challenging problem of estimating the general visualattention of people in images. Our proposed method is designed to work acrossmultiple naturalistic social scenarios and provides a full picture of thesubject's attention and gaze. In contrast, earlier works on gaze and attentionestimation have focused on constrained problems in more specific contexts. Inparticular, our model explicitly represents the gaze direction and handlesout-of-frame gaze targets. We leverage three different datasets using amulti-task learning approach. We evaluate our method on widely used benchmarksfor single-tasks such as gaze angle estimation and attention-within-an-image,as well as on the new challenging task of generalized visual attentionprediction. In addition, we have created extended annotations for the MMDB andGazeFollow datasets which are used in our experiments, which we will publiclyrelease.

Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency