Papers › ViC-MAE: Self-Supervised Representation Learning from Images and Video with...
ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders
Jefferson Hernandez, Ruben Villegas, Vicente Ordonez
We propose ViC-MAE, a model that combines both Masked AutoEncoders (MAE) and contrastive learning. ViC-MAE is trained using a global featured obtained by pooling the local representations learned under an MAE reconstruction loss and leveraging this representation under a contrastive objective across images and video frames. We show that visual representations learned under ViC-MAE generalize well to both video and image classification tasks. Particularly, ViC-MAE obtains state-of-the-art transfer learning performance from video to images on Imagenet-1k compared to the recently proposed OmniMAE by achieving a top-1 accuracy of 86% (+1.3% absolute improvement) when trained on the same data and 87.1% (+2.4% absolute improvement) when training on extra data. At the same time ViC-MAE outperforms most other methods on video benchmarks by obtaining 75.9% top-1 accuracy on the challenging Something something-v2 video benchmark . When training on videos and images from a diverse combination of datasets, our method maintains a balanced transfer-learning performance between video and image classification benchmarks, coming only as a close second to the best supervised method.
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Code
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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 | ViC-MAE (ViT-L) | Acc@1 | 85.1 | #58 of 207 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | ViC-MAE (ViT-L) | Top-1 Accuracy | 73.7 | #18 of 123 | Archive leaderboard | report |
| Image Classification | ImageNet | ViC-MAE (ViT-L) | Top 1 Accuracy | 85% | #264 of 1060 | Archive leaderboard | report |
| Image Classification | Places365 | ViC-MAE (ViT-L) | Top 1 Accuracy | 59.5% | #5 of 7 | 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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