DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Prediction

TL;DR

This paper introduces DAVE, a simple deep learning model that integrates audio and visual cues to improve dynamic saliency prediction in videos, demonstrating audio's significant contribution to gaze prediction accuracy.

Contribution

The paper presents a novel, simple deep audio-visual embedding model for dynamic saliency prediction and provides an extensive analysis of audio's role in enhancing saliency models.

Findings

Audio significantly improves saliency prediction accuracy.

Salient sound sources naturally attract visual attention.

Audio-visual model outperforms visual-only models on over 53% of frames.

Abstract

This paper studies audio-visual deep saliency prediction. It introduces a conceptually simple and effective Deep Audio-Visual Embedding for dynamic saliency prediction dubbed ``DAVE" in conjunction with our efforts towards building an Audio-Visual Eye-tracking corpus named ``AVE". Despite existing a strong relation between auditory and visual cues for guiding gaze during perception, video saliency models only consider visual cues and neglect the auditory information that is ubiquitous in dynamic scenes. Here, we investigate the applicability of audio cues in conjunction with visual ones in predicting saliency maps using deep neural networks. To this end, the proposed model is intentionally designed to be simple. Two baseline models are developed on the same architecture which consists of an encoder-decoder. The encoder projects the input into a feature space followed by a decoder that infers saliency. We conduct an extensive analysis on different modalities and various aspects of multi-model dynamic saliency prediction. Our results suggest that (1) audio is a strong contributing cue for saliency prediction, (2) salient visible sound-source is the natural cause of the superiority of our Audio-Visual model, (3) richer feature representations for the input space leads to more powerful predictions even in absence of more sophisticated saliency decoders, and (4) Audio-Visual model improves over 53.54\% of the frames predicted by the best Visual model (our baseline). Our endeavour demonstrates that audio is an important cue that boosts dynamic video saliency prediction and helps models to approach human performance. The code is available at https://github.com/hrtavakoli/DAVE