Broaden Your Views for Self-Supervised Video Learning
This addresses video representation learning for tasks like classification, offering a novel approach that improves over existing methods.
The paper tackled the problem of self-supervised learning in video by introducing BraVe, a framework that uses narrow and broad temporal views to leverage time, achieving state-of-the-art results on benchmarks like UCF101, HMDB51, Kinetics, ESC-50, and AudioSet.
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.