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AViNet: Diving Deep into Audio-Visual Saliency Prediction

2020-12-11 07:28:02
Samyak Jain, Pradeep Yarlagadda, Ramanathan Subramanian, Vineet Gandhi

Abstract

We propose the \textbf{AViNet} architecture for audiovisual saliency prediction. AViNet is a fully convolutional encoder-decoder architecture. The encoder combines visual features learned for action recognition, with audio embeddings learned via an aural network designed to classify objects and scenes. The decoder infers a saliency map via trilinear interpolation and 3D convolutions, combining hierarchical features. The overall architecture is conceptually simple, causal, and runs in real-time (60 fps). AViNet outperforms the state-of-the-art on ten (seven audiovisual and three visual-only) datasets while surpassing human performance on the CC, SIM, and AUC metrics for the AVE dataset. Visual features maximally account for saliency on existing datasets with audio-only contributing to minor gains, except in specific contexts like social events. Our work, therefore, motivates the need to curate saliency datasets reflective of real-life, where both the visual and aural modalities complimentarily drive saliency. Our code and pre-trained models are available at this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2012.06170

PDF

https://arxiv.org/pdf/2012.06170.pdf


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