Papers › VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
Hao Tan, Jie Lei, Thomas Wolf, Mohit Bansal
Video understanding relies on perceiving the global content and modeling its internal connections (e.g., causality, movement, and spatio-temporal correspondence). To learn these interactions, we apply a mask-then-predict pre-training task on discretized video tokens generated via VQ-VAE. Unlike language, where the text tokens are more independent, neighboring video tokens typically have strong correlations (e.g., consecutive video frames usually look very similar), and hence uniformly masking individual tokens will make the task too trivial to learn useful representations. To deal with this issue, we propose a block-wise masking strategy where we mask neighboring video tokens in both spatial and temporal domains. We also add an augmentation-free contrastive learning method to further capture the global content by predicting whether the video clips are sampled from the same video. We pre-train our model on uncurated videos and show that our pre-trained model can reach state-of-the-art results on several video understanding datasets (e.g., SSV2, Diving48). Lastly, we provide detailed analyses on model scalability and pre-training method design. Code is released at https://github.com/airsplay/vimpac.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Classification | Kinetics-400 | VIMPAC | Acc@1 | 77.4 | #142 of 207 | Archive leaderboard | report |
| Action Recognition | Diving-48 | VIMPAC | Accuracy | 85.5 | #11 of 18 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | VIMPAC | Average accuracy of 3 splits | 65.9 | #61 of 77 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | VIMPAC | Top-1 Accuracy | 68.1 | #55 of 123 | Archive leaderboard | report |
| Action Recognition | UCF101 | VIMPAC | 3-fold Accuracy | 92.7 | #62 of 91 | 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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