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Contrastive Audio-Visual Masked Autoencoder. (arXiv:2210.07839v1 [cs.MM])
Oct. 17, 2022, 1:16 a.m. | Yuan Gong, Andrew Rouditchenko, Alexander H. Liu, David Harwath, Leonid Karlinsky, Hilde Kuehne, James Glass
cs.CV updates on arXiv.org arxiv.org
In this paper, we first extend the recent Masked Auto-Encoder (MAE) model
from a single modality to audio-visual multi-modalities. Subsequently, we
propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining
contrastive learning and masked data modeling, two major self-supervised
learning frameworks, to learn a joint and coordinated audio-visual
representation. Our experiments show that the contrastive audio-visual
correspondence learning objective not only enables the model to perform
audio-visual retrieval tasks, but also helps the model learn a better joint
representation. As …
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