Lifestyle, Special Report, Americas

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The joint optimization scheme can significantly improve the spatio-temporal representation in video understanding. Convolutional Neural Networks (CNNs) have been proven to be successful in supervised video representation learning with numerous human-annotated labels (Carreira and Zisserman 2017; Feichtenhofer et al. 2019). Videos contain more complex spatio-temporal contents and a larger data volume. Billions of unlabeled videos emerge on the Internet every day, making supervised video analysis expensive and time-consuming. Thus, how to effectively learn video representations without annotations is an important yet challenging task. Among effective unsupervised learning methods, self-supervised learning has proven to be a promising methodology (Chen et al. 2020; He et al. 2020; Feichtenhofer et al. Early video self-supervised learning approaches proposed proper tasks with automatically generated labels, thereby encouraging CNNs to learn the transferable features for downstream tasks without human-annotated labels (Fernando et al. 2017; Benaim et al. 2020; Behrmann, Gall, and Noroozi 2021). Recently, with the success of contrastive learning in self-supervised image classification, this method has been widely expended in video self-supervised learning (Chen et al.




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