  In this work, we explore regions as a potential visual analogue of words for
self-supervised image representation learning. Inspired by Masked Autoencoding
(MAE), a generative pre-training baseline, we propose masked region
autoencoding to learn from groups of pixels or regions. Specifically, we design
an architecture which efficiently addresses the one-to-many mapping between
images and regions, while being highly effective especially with high-quality
regions. When integrated with MAE, our approach (R-MAE) demonstrates consistent
improvements across various pre-training datasets and downstream detection and
segmentation benchmarks, with negligible computational overheads. Beyond the
quantitative evaluation, our analysis indicates the models pre-trained with
masked region autoencoding unlock the potential for interactive segmentation.
The code is provided at https://github.com/facebookresearch/r-mae.
