MultiMAE: Multi-modal Multi-task Masked Autoencoders
We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects:
- It can optionally accept additional modalities of information in the input beside RGB images (hence “multi-modal”).
- Its training objective accordingly includes predicting multiple outputs besides reconstructing the RGB image (hence “multi-task”).
We make use of masking (across image patches and input modalities) to make training MultiMAE tractable as well as to ensure cross-modality predictive coding is indeed learned by the network. We show this pre-training strategy leads to a flexible, simple, and efficient framework with improved transfer results to downstream tasks. In particular, the same exact pre-trained network can be flexibly used when additional information besides RGB images is available or when no information other than RGB is available – in all configurations yielding competitive to or significantly better results than the baselines. To avoid needing training datasets with multiple modalities and annotated tasks, we train MultiMAE entirely using pseudo labeling, which makes framework widely applicable to any RGB dataset.
The experiments are performed on multiple transfer tasks (image classification, semantic segmentation, depth estimation) and datasets (ImageNet, ADE20K, Taskonomy, Hypersim, NYUv2). The results show an intriguingly impressive capability by the model in cross-modal/task predictive coding and transfer. Please see our Github repository for code and pre-trained models and our website for interactive visualizations!