Papers › Self-Supervised Video Representation Learning with Meta-Contrastive Network
Self-Supervised Video Representation Learning with Meta-Contrastive Network
Yuanze Lin, Xun Guo, Yan Lu
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Action Recognition | HMDB51 | MCN (R3D-18; RGB) | Frozen | false | #28 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | MCN (R3D-18; RGB) | Pre-Training Dataset | UCF101 | #28 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | MCN (R3D-18; RGB) | Top-1 Accuracy | 54.8 | #28 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | MCN (R2+1D; RGB) | Frozen | false | #29 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | MCN (R2+1D; RGB) | Pre-Training Dataset | UCF101 | #29 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | MCN (R2+1D; RGB) | Top-1 Accuracy | 54.5 | #29 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | MCN (R3D-18; RGB) | 3-fold Accuracy | 85.4 | #28 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | MCN (R2+1D; RGB) | 3-fold Accuracy | 84.8 | #29 of 53 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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