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[arXiv]
[bibtex]@InProceedings{Recasens_2021_ICCV, author = {Recasens, Adri\`a and Luc, Pauline and Alayrac, Jean-Baptiste and Wang, Luyu and Strub, Florian and Tallec, Corentin and Malinowski, Mateusz and P\u{a}tr\u{a}ucean, Viorica and Altch\'e, Florent and Valko, Michal and Grill, Jean-Bastien and van den Oord, A\"aron and Zisserman, Andrew}, title = {Broaden Your Views for Self-Supervised Video Learning}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {1255-1265} }
Broaden Your Views for Self-Supervised Video Learning
Abstract
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cropping and augmenting the resulting crop. However, these methods miss a crucial element in the video domain: time. We introduce BraVe, a self-supervised learning framework for video. In BraVe, one of the views has access to a narrow temporal window of the video while the other view has a broad access to the video content. Our models learn to generalise from the narrow view to the general content of the video. Furthermore, BraVe processes the views with different backbones, enabling the use of alternative augmentations or modalities into the broad view such as optical flow, randomly convolved RGB frames, audio or their combinations. We demonstrate that BraVe achieves state-of-the-art results in self-supervised representation learning on standard video and audio classification benchmarks including UCF101, HMDB51, Kinetics, ESC-50 and AudioSet.
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