R-MAE: Regions Meet Masked Autoencoders
Authors: name: Duy-Kien Nguyen, name: Vaibhav Aggarwal, name: Yanghao Li, name: Martin R. Oswald, name: Alexander Kirillov, name: Cees G. M. Snoek, name: Xinlei Chen

Vision-specific concepts such as "region" have played a key role in extending
general machine learning frameworks to tasks like object detection. Given the
success of region-based detectors for supervised learning and the progress of
intra-image methods for contrastive learning, we explore the use of regions for
reconstructive pre-training. Starting from Masked Autoencoding (MAE) both as a
baseline and an inspiration, we propose a parallel pre-text task tailored to
address the one-to-many mapping between images and regions. Since such regions
can be generated in an unsupervised way, our approach (R-MAE) inherits the wide
applicability from MAE, while being more "region-aware". We conduct thorough
analyses during the development of R-MAE, and converge on a variant that is
both effective and efficient (1.3% overhead over MAE). Moreover, it shows
consistent quantitative improvements when generalized to various pre-training
data and downstream detection and segmentation benchmarks. Finally, we provide
extensive qualitative visualizations to enhance the understanding of R-MAE's
behaviour and potential. Code will be made available at
https://github.com/facebookresearch/r-mae.