Papers › Masked Feature Prediction for Self-Supervised Visual Pre-Training
Masked Feature Prediction for Self-Supervised Visual Pre-Training
Chen Wei, Haoqi Fan, Saining Xie, Chao-yuan Wu, Alan Yuille, Christoph Feichtenhofer
We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We study five different types of features and find Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, works particularly well in terms of both performance and efficiency. We observe that the local contrast normalization in HOG is essential for good results, which is in line with earlier work using HOG for visual recognition. Our approach can learn abundant visual knowledge and drive large-scale Transformer-based models. Without using extra model weights or supervision, MaskFeat pre-trained on unlabeled videos achieves unprecedented results of 86.7% with MViT-L on Kinetics-400, 88.3% on Kinetics-600, 80.4% on Kinetics-700, 39.8 mAP on AVA, and 75.0% on SSv2. MaskFeat further generalizes to image input, which can be interpreted as a video with a single frame and obtains competitive results on ImageNet.
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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 | MaskFeat (K600, MViT-L) | Acc@1 | 87.0 | #42 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | MaskFeat (K600, MViT-L) | Acc@5 | 97.4 | #42 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | MaskFeat (no extra data, MViT-L) | Acc@1 | 86.7 | #45 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | MaskFeat (no extra data, MViT-L) | Acc@5 | 97.3 | #45 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-600 | MaskFeat (no extra data, MViT-L) | Top-1 Accuracy | 88.3 | #20 of 65 | Archive leaderboard | report |
| Action Classification | Kinetics-600 | MaskFeat (no extra data, MViT-L) | Top-5 Accuracy | 98.0 | #20 of 65 | Archive leaderboard | report |
| Action Classification | Kinetics-700 | MaskFeat (no extra data, MViT-L) | Top-1 Accuracy | 80.4 | #12 of 36 | Archive leaderboard | report |
| Action Classification | Kinetics-700 | MaskFeat (no extra data, MViT-L) | Top-5 Accuracy | 95.7 | #12 of 36 | Archive leaderboard | report |
| Action Recognition | AVA v2.2 | MaskFeat (Kinetics-600 pretrain, MViT-L) | mAP | 39.8 | #8 of 38 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | MaskFeat (Kinetics600 pretrain, MViT-L) | GFLOPs | 2828*3 | #11 of 123 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | MaskFeat (Kinetics600 pretrain, MViT-L) | Parameters | 218 | #11 of 123 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | MaskFeat (Kinetics600 pretrain, MViT-L) | Top-1 Accuracy | 75.0 | #11 of 123 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | MaskFeat (Kinetics600 pretrain, MViT-L) | Top-5 Accuracy | 95.0 | #11 of 123 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet (finetuned) | MaskFeat (ViT-L) | Number of Params | 307M | #21 of 65 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet (finetuned) | MaskFeat (ViT-L) | Top 1 Accuracy | 85.7% | #21 of 65 | 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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