Video Contrastive Learning with Global Context

Abstract

Contrastive learning has revolutionized self-supervised image representation learning field, and recently been adapted to video domain. One of the greatest advantages of contrastive learning is that it allows us to flexibly define powerful loss objectives as long as we can find a reasonable way to formulate positive and negative samples to contrast. However, existing approaches rely heavily on the short-range spatiotemporal salience to form clip-level contrastive signals, thus limit themselves from using global context. In this paper, we propose a new video-level contrastive learning method based on segments to formulate positive pairs. Our formulation is able to capture global context in a video, thus robust to temporal content change. We also incorporate a temporal order regularization term to enforce the inherent sequential structure of videos. Extensive experiments show that our video-level contrastive learning framework (VCLR) is able to outperform previous state-of-the-arts on five video datasets for downstream action classification, action localization and video retrieval. Code is available at https://github.com/amazon-research/video-contrastive-learning.

Code is publicly available at: https://github.com/amazon-research/video-contrastive-learning

References in corpus (9)

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• Bootstrap your own latent: A new approach to self-supervised Learning
• The Kinetics Human Action Video Dataset
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• VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples
• RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning