  Unsupervised domain adaptation which aims to adapt models trained on a
labeled source domain to a completely unlabeled target domain has attracted
much attention in recent years. While many domain adaptation techniques have
been proposed for images, the problem of unsupervised domain adaptation in
videos remains largely underexplored. In this paper, we introduce Contrast and
Mix (CoMix), a new contrastive learning framework that aims to learn
discriminative invariant feature representations for unsupervised video domain
adaptation. First, unlike existing methods that rely on adversarial learning
for feature alignment, we utilize temporal contrastive learning to bridge the
domain gap by maximizing the similarity between encoded representations of an
unlabeled video at two different speeds as well as minimizing the similarity
between different videos played at different speeds. Second, we propose a novel
extension to the temporal contrastive loss by using background mixing that
allows additional positives per anchor, thus adapting contrastive learning to
leverage action semantics shared across both domains. Moreover, we also
integrate a supervised contrastive learning objective using target
pseudo-labels to enhance discriminability of the latent space for video domain
adaptation. Extensive experiments on several benchmark datasets demonstrate the
superiority of our proposed approach over state-of-the-art methods. Project
page: https://cvir.github.io/projects/comix
