  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.
