  Learning self-supervised video representation predominantly focuses on
discriminating instances generated from simple data augmentation schemes.
However, the learned representation often fails to generalize over unseen
camera viewpoints. To this end, we propose ViewCLR, that learns self-supervised
video representation invariant to camera viewpoint changes. We introduce a
view-generator that can be considered as a learnable augmentation for any
self-supervised pre-text tasks, to generate latent viewpoint representation of
a video. ViewCLR maximizes the similarities between the latent viewpoint
representation with its representation from the original viewpoint, enabling
the learned video encoder to generalize over unseen camera viewpoints.
Experiments on cross-view benchmark datasets including NTU RGB+D dataset show
that ViewCLR stands as a state-of-the-art viewpoint invariant self-supervised
method.
