Self-Supervised Video Representation Learning with Meta-Contrastive Network

• URL: http://arxiv.org/abs/2108.08426v2
• Date: Mon, 23 Aug 2021 16:41:06 GMT
• Title: Self-Supervised Video Representation Learning with Meta-Contrastive Network
• Authors: Yuanze Lin, Xun Guo, Yan Lu
• Abstract summary: We propose a Meta-Contrastive Network (MCN) to enhance the learning ability of self-supervised approaches. For two downstream tasks, i.e., video action recognition and video retrieval, MCN outperforms state-of-the-art approaches.
• Score: 10.768575680990415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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