Papers › Self-supervised Co-training for Video Representation Learning
Self-supervised Co-training for Video Representation Learning
Tengda Han, Weidi Xie, Andrew Zisserman
The objective of this paper is visual-only self-supervised video representation learning. We make the following contributions: (i) we investigate the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation (InfoNCE) training, showing that this form of supervised contrastive learning leads to a clear improvement in performance; (ii) we propose a novel self-supervised co-training scheme to improve the popular infoNCE loss, exploiting the complementary information from different views, RGB streams and optical flow, of the same data source by using one view to obtain positive class samples for the other; (iii) we thoroughly evaluate the quality of the learnt representation on two different downstream tasks: action recognition and video retrieval. In both cases, the proposed approach demonstrates state-of-the-art or comparable performance with other self-supervised approaches, whilst being significantly more efficient to train, i.e. requiring far less training data to achieve similar performance.
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Code
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Code Syntology ran Syntology
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5a36a54b4371cc75 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Action Recognition | HMDB51 | CoCLR | Frozen | false | #34 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | CoCLR | Top-1 Accuracy | 46.1 | #34 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 (finetuned) | CoCLR | Top-1 Accuracy | 54.6 | #12 of 14 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | CoCLR | 3-fold Accuracy | 74.5 | #34 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | CoCLR | Frozen | false | #34 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 (finetuned) | CoCLR | 3-fold Accuracy | 87.9 | #12 of 14 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 (finetuned) | CoCLR | Pretrain | K400 | #12 of 14 | 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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