717 research outputs found
Contrastive Audio-Visual Masked Autoencoder
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 a result, our fully self-supervised pretrained CAV-MAE
achieves a new SOTA accuracy of 65.9% on VGGSound, and is comparable with the
previous best supervised pretrained model on AudioSet in the audio-visual event
classification task. Code and pretrained models are at
https://github.com/yuangongnd/cav-mae.Comment: Accepted at ICLR 2023 as a notable top 25% paper. Code and pretrained
models are at https://github.com/yuangongnd/cav-ma
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