Masked Autoencoders As Spatiotemporal Learners

TL;DR

This paper extends Masked Autoencoders to spatiotemporal video data, demonstrating that high masking ratios and minimal inductive bias enable effective representation learning and significant speedups, outperforming supervised methods.

Contribution

It introduces a simple yet effective spatiotemporal MAE framework that requires minimal domain knowledge and achieves strong results on video datasets.

Findings

High masking ratio (up to 90%) is optimal for videos.

MAE outperforms supervised pre-training on several datasets.

Training on uncurated Instagram data yields promising results.

Abstract

This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to spatiotemporal representation learning from videos. We randomly mask out spacetime patches in videos and learn an autoencoder to reconstruct them in pixels. Interestingly, we show that our MAE method can learn strong representations with almost no inductive bias on spacetime (only except for patch and positional embeddings), and spacetime-agnostic random masking performs the best. We observe that the optimal masking ratio is as high as 90% (vs. 75% on images), supporting the hypothesis that this ratio is related to information redundancy of the data. A high masking ratio leads to a large speedup, e.g., > 4x in wall-clock time or even more. We report competitive results on several challenging video datasets using vanilla Vision Transformers. We observe that MAE can outperform supervised pre-training by large margins. We further report encouraging results of training on real-world, uncurated Instagram data. Our study suggests that the general framework of masked autoencoding (BERT, MAE, etc.) can be a unified methodology for representation learning with minimal domain knowledge.