Papers › SimVTP: Simple Video Text Pre-training with Masked Autoencoders
SimVTP: Simple Video Text Pre-training with Masked Autoencoders
Yue Ma, Tianyu Yang, Yin Shan, Xiu Li
This paper presents SimVTP: a Simple Video-Text Pretraining framework via masked autoencoders. We randomly mask out the spatial-temporal tubes of input video and the word tokens of input text and then feed them into a unified autencoder to reconstruct the missing pixels and words. Our SimVTP has several properties: 1) Thanks to the unified autoencoder, SimVTP reconstructs the masked signal of one modality with the help from another modality, which implicitly learns the cross-modal alignment between video tubes and text tokens. 2) SimVTP not only benefits from a high video masking ratio (e.g. 90%) due to the temporal redundancy of video, but also needs a high text masking ratio (e.g. 75%), which is much higher than BERT (e.g. 15%), to achieve optimal performance. This is because the aid of video modality makes text reconstruction less challenging, which thus needs a higher mask ratio to make the pretext harder for useful feature learning. 3) Equipping SimVTP with video-text contrastive learning (VTC) and video-text matching (VTM), which are two commonly used cross-modal training strategies, could further improve the transferable performance significantly. 4) SimVTP is dataefficent, e.g., pre-training only on 10% data of WebVid-2M, SimVTP achieves surprisingly good results (43.8 R@1) on MSRVTT, which is far above recent state-of-the-art methods pre-trained on both CC3M and WebVid-2M. We transfer our pre-trained model to various downstream tasks and achieve superior performance. The codes and models will be released at https://github.com/mayuelala/SimVTP.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Moment Retrieval | Charades-STA | SimVTP | R@1 IoU=0.5 | 44.7 | #25 of 25 | Archive leaderboard | report |
| Moment Retrieval | Charades-STA | SimVTP | R@1 IoU=0.7 | 26.3 | #25 of 25 | Archive leaderboard | report |
| Moment Retrieval | Charades-STA | SimVTP | R@5 IoU=0.5 | 83.7 | #25 of 25 | Archive leaderboard | report |
| Moment Retrieval | Charades-STA | SimVTP | R@5 IoU=0.7 | 55.1 | #25 of 25 | 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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