Survey of self-supervised learning for videos
Survey of self-supervised learning for videos
Self-Supervised Learning for Videos: A Survey
arXiv paper abstract https://arxiv.org/abs/2207.00419
arXiv PDF paper https://arxiv.org/pdf/2207.00419.pdf
... self-supervised learning provides a way for representation learning which does not require annotations and has shown promise in both image and video domains.
Different from the image domain, learning video representations are more challenging due to the temporal dimension, bringing in motion and other environmental dynamics.
This also provides opportunities for video-exclusive ideas that advance self-supervised learning in the video and multimodal domain.
In this survey, ... provide a review of existing approaches on self-supervised learning focusing on the video domain.
... summarize these methods into four different categories based on their learning objectives: 1) pretext tasks, 2) generative learning, 3) contrastive learning, and 4) cross-modal agreement.
... further introduce the commonly used datasets, downstream evaluation tasks, insights into the limitations of existing works, and the potential future directions in this area.
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