Papers › Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Bruno Korbar, Du Tran, Lorenzo Torresani
There is a natural correlation between the visual and auditive elements of a video. In this work we leverage this connection to learn general and effective models for both audio and video analysis from self-supervised temporal synchronization. We demonstrate that a calibrated curriculum learning scheme, a careful choice of negative examples, and the use of a contrastive loss are critical ingredients to obtain powerful multi-sensory representations from models optimized to discern temporal synchronization of audio-video pairs. Without further finetuning, the resulting audio features achieve performance superior or comparable to the state-of-the-art on established audio classification benchmarks (DCASE2014 and ESC-50). At the same time, our visual subnet provides a very effective initialization to improve the accuracy of video-based action recognition models: compared to learning from scratch, our self-supervised pretraining yields a remarkable gain of +19.9% in action recognition accuracy on UCF101 and a boost of +17.7% on HMDB51.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Classification | ESC-50 | AVTS | Top-1 Accuracy | 82.3 | #28 of 29 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 (finetuned) | AVTS | Top-1 Accuracy | 61.6 | #10 of 14 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 (finetuned) | AVTS | 3-fold Accuracy | 89.0 | #10 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.
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