  Self-supervised learning has been successfully applied to pre-train video
representations, which aims at efficient adaptation from pre-training domain to
downstream tasks. Existing approaches merely leverage contrastive loss to learn
instance-level discrimination. However, lack of category information will lead
to hard-positive problem that constrains the generalization ability of this
kind of methods. We find that the multi-task process of meta learning can
provide a solution to this problem. In this paper, we propose a
Meta-Contrastive Network (MCN), which combines the contrastive learning and
meta learning, to enhance the learning ability of existing self-supervised
approaches. Our method contains two training stages based on model-agnostic
meta learning (MAML), each of which consists of a contrastive branch and a meta
branch. Extensive evaluations demonstrate the effectiveness of our method. For
two downstream tasks, i.e., video action recognition and video retrieval, MCN
outperforms state-of-the-art approaches on UCF101 and HMDB51 datasets. To be
more specific, with R(2+1)D backbone, MCN achieves Top-1 accuracies of 84.8%
and 54.5% for video action recognition, as well as 52.5% and 23.7% for video
retrieval.
