Self-Supervised MultiModal Versatile Networks

• URL: http://arxiv.org/abs/2006.16228v2
• Date: Fri, 30 Oct 2020 17:53:59 GMT
• Title: Self-Supervised MultiModal Versatile Networks
• Authors: Jean-Baptiste Alayrac, Adri\`a Recasens, Rosalia Schneider, Relja Arandjelovi\'c, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman
• Abstract summary: We learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks.
• Score: 76.19886740072808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple modalities and whose representations enable downstream tasks in multiple modalities. In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding. Driven by versatility, we also introduce a novel process of deflation, so that the networks can be effortlessly applied to the visual data in the form of video or a static image. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks. Equipped with these representations, we obtain state-of-the-art performance on multiple challenging benchmarks including UCF101, HMDB51, Kinetics600, AudioSet and ESC-50 when compared to previous self-supervised work. Our models are publicly available.

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