Papers › Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking
Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking
Alaaeldin El-Nouby, Shuangfei Zhai, Graham W. Taylor, Joshua M. Susskind
Deep neural networks require collecting and annotating large amounts of data to train successfully. In order to alleviate the annotation bottleneck, we propose a novel self-supervised representation learning approach for spatiotemporal features extracted from videos. We introduce Skip-Clip, a method that utilizes temporal coherence in videos, by training a deep model for future clip order ranking conditioned on a context clip as a surrogate objective for video future prediction. We show that features learned using our method are generalizable and transfer strongly to downstream tasks. For action recognition on the UCF101 dataset, we obtain 51.8% improvement over random initialization and outperform models initialized using inflated ImageNet parameters. Skip-Clip also achieves results competitive with state-of-the-art self-supervision methods.
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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 | UCF101 | Skip-Clip (3D ResNet-18) | 3-fold Accuracy | 64.4 | #44 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | Skip-Clip (3D ResNet-18) | Frozen | false | #44 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | Skip-Clip (3D ResNet-18) | Pre-Training Dataset | UCF101 | #44 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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