  Recently, video streams have occupied a large proportion of Internet traffic,
most of which contain human faces. Hence, it is necessary to predict saliency
on multiple-face videos, which can provide attention cues for many content
based applications. However, most of multiple-face saliency prediction works
only consider visual information and ignore audio, which is not consistent with
the naturalistic scenarios. Several behavioral studies have established that
sound influences human attention, especially during the speech turn-taking in
multiple-face videos. In this paper, we thoroughly investigate such influences
by establishing a large-scale eye-tracking database of Multiple-face Video in
Visual-Audio condition (MVVA). Inspired by the findings of our investigation,
we propose a novel multi-modal video saliency model consisting of three
branches: visual, audio and face. The visual branch takes the RGB frames as the
input and encodes them into visual feature maps. The audio and face branches
encode the audio signal and multiple cropped faces, respectively. A fusion
module is introduced to integrate the information from three modalities, and to
generate the final saliency map. Experimental results show that the proposed
method outperforms 11 state-of-the-art saliency prediction works. It performs
closer to human multi-modal attention.
