  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
