  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.
