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The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning
Hence, we extensively investigate composition of temporal augmentations suitable for learning audiovisual representations; we find lossy spatio-temporal transformations that do not corrupt the temporal coherency of videos are the most effective.
CLAR: Contrastive Learning of Auditory Representations
We illustrate that by combining all these methods and with substantially less labeled data, our framework (CLAR) achieves significant improvement on prediction performance compared to supervised approach.
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