Self-Supervised Learning for Videos: A Survey
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The remarkable success of deep learning in various domains relies on the availability of large-scale annotated datasets. However, the use of human-generated annotations leads to models with biased learning, poor domain generalization, and poor robustness. Obtaining annotations is also expensive and requires great effort, which is especially challenging for videos. As an alternative, 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 exclusive ideas which can advance self-supervised learning in the video and multimodal domain. In this survey, we provide a review of existing approaches on self-supervised learning focusing on the video domain. We summarize these methods into three different categories based on their learning objectives: pre-text tasks, generative modeling, and contrastive learning. These approaches also differ in terms of the modality which are being used: video, video-audio, video-text, and video-audio-text. We 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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