  Joint visual attention is characterized by two or more individuals looking at
a common target at the same time. The ability to identify joint attention in
scenes, the people involved, and their common target, is fundamental to the
understanding of social interactions, including others' intentions and goals.
In this work we deal with the extraction of joint attention events, and the use
of such events for image descriptions. The work makes two novel contributions.
First, our extraction algorithm is the first which identifies joint visual
attention in single static images. It computes 3D gaze direction, identifies
the gaze target by combining gaze direction with a 3D depth map computed for
the image, and identifies the common gaze target. Second, we use a human study
to demonstrate the sensitivity of humans to joint attention, suggesting that
the detection of such a configuration in an image can be useful for
understanding the image, including the goals of the agents and their joint
activity, and therefore can contribute to image captioning and related tasks.
