Video data. Most of SSL images methods have a counter-part video SSL method. For
instance, Feichtenhofer et al. [2021] have generalized SimCLR, MoCo, SwAV and BYOL to
space-time video data. Indeed, in all these methods it is possible to incorporate the notion
of similarity between diﬀerent temporal clips of the same video. More recently, masked
auto-encoding objectives for video have been built around the same idea as images, but
by masking patches/ tubes of patches in the temporal axis as well [Feichtenhofer et al.,
2022, Tong et al., 2022, Girdhar et al., 2022]. Besides, it is common practice to use SSL
vision pre-trained models for video downstream tasks like action recognition. With ViT for
instance, the patch embedding convolutional layer can be transferred from 2D to 3D by
repeating the weights along the temporal axis [Feichtenhofer et al., 2022]. Vision models
can then transfer to video models by using them as initialization for ﬁne-tuning on video
A Cookbook of Self-Supervised Learning