  We propose a pre-training strategy called Multi-modal Multi-task Masked
Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two
key aspects: I) it can optionally accept additional modalities of information
in the input besides the RGB image (hence "multi-modal"), and II) its training
objective accordingly includes predicting multiple outputs besides 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
tasks, we train MultiMAE entirely using pseudo labeling, which makes the
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.
