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Generating representations of video data is of key importance in advancingthe field of machine perception. Most current techniques rely on hand-annotateddata, which can be difficult to work with, expensive to generate, and hard toscale. In this work, we propose a novel learning approach based on contrastivelearning, LAVA, which is capable of learning joint language, audio, and videorepresentations in a self-supervised manner. We pre-train LAVA on the Kinetics700 dataset using transformer encoders to learn representations for eachmodality. We then demonstrate that LAVA performs competitively with the currentstate-of-the-art self-supervised and weakly-supervised pretraining techniqueson UCF-101 and HMDB-51 video action recognition while using a fraction of theunlabeled data.

LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training