Audiovisual Masked Autoencoders

• URL: http://arxiv.org/abs/2212.05922v3
• Date: Thu, 4 Jan 2024 16:52:34 GMT
• Title: Audiovisual Masked Autoencoders
• Authors: Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu, Mario Lucic, Cordelia Schmid, Anurag Arnab
• Abstract summary: We show that we can achieve significant improvements on audiovisual downstream classification tasks. We additionally demonstrate the transferability of our representations, achieving state-of-the-art audiovisual results on Epic Kitchens.
• Score: 93.22646144125457
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can we leverage the audiovisual information already present in video to improve self-supervised representation learning? To answer this question, we study various pretraining architectures and objectives within the masked autoencoding framework, motivated by the success of similar methods in natural language and image understanding. We show that we can achieve significant improvements on audiovisual downstream classification tasks, surpassing the state-of-the-art on VGGSound and AudioSet. Furthermore, we can leverage our audiovisual pretraining scheme for multiple unimodal downstream tasks using a single audiovisual pretrained model. We additionally demonstrate the transferability of our representations, achieving state-of-the-art audiovisual results on Epic Kitchens without pretraining specifically for this dataset.

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