Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model

TL;DR

This paper introduces a multi-modal saliency prediction model for multiple-face videos that incorporates visual, audio, and face information, demonstrating improved accuracy over existing methods and aligning more closely with human attention patterns.

Contribution

The paper presents a novel multi-modal saliency model that integrates visual, audio, and face cues, supported by a large-scale eye-tracking database and outperforming existing methods.

Findings

Outperforms 11 state-of-the-art saliency models

Aligns closely with human multi-modal attention

Validates the influence of audio on visual saliency

Abstract

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.