Towards a behaviorally-validated computational audiovisual saliency model

Abstract

Computational saliency models aim at predicting, in a bottom-up fashion, where human attention is drawn in the presented (visual, auditory or audiovisual) scene and have been proven useful in applications like robotic navigation, image compression and movie summarization. Despite the fact that well-established auditory and visual saliency models have been validated in behavioral experiments, e.g., by means of eye-tracking, there is no established computational audiovisual saliency model validated in the same way. In this work, building on biologically-inspired models of visual and auditory saliency, we present a joint audiovisual saliency model and introduce the validation approach we follow to show that it is compatible with recent findings of psychology and neuroscience regarding multimodal integration and attention. In this direction, we initially focus on the pip and pop effect which has been observed in behavioral experiments and indicates that visual search in sequences of cluttered images can be significantly aided by properly timed non-spatial auditory signals presented alongside the target visual stimuli.