MAViL: Masked Audio-Video Learners

• URL: http://arxiv.org/abs/2212.08071v2
• Date: Mon, 17 Jul 2023 05:44:35 GMT
• Title: MAViL: Masked Audio-Video Learners
• Authors: Po-Yao Huang, Vasu Sharma, Hu Xu, Chaitanya Ryali, Haoqi Fan, Yanghao Li, Shang-Wen Li, Gargi Ghosh, Jitendra Malik, Christoph Feichtenhofer
• Abstract summary: We present Masked Audio-Video learners (MAViL) to train audio-visual representations. Pre-training with MAViL enables the model to perform well in audio-visual classification and retrieval tasks. For the first time, a self-supervised audio-visual model outperforms ones that use external supervision on benchmarks.
• Score: 68.61844803682145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Masked Audio-Video Learners (MAViL) to train audio-visual representations. Our approach learns with three complementary forms of self-supervision: (1) reconstruction of masked audio and video input data, (2) intra- and inter-modal contrastive learning with masking, and (3) self-training by reconstructing joint audio-video contextualized features learned from the first two objectives. Pre-training with MAViL not only enables the model to perform well in audio-visual classification and retrieval tasks but also improves representations of each modality in isolation, without using information from the other modality for fine-tuning or inference. Empirically, MAViL sets a new state-of-the-art on AudioSet (53.1 mAP) and VGGSound (67.1% accuracy). For the first time, a self-supervised audio-visual model outperforms ones that use external supervision on these benchmarks.

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