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R-MAE: Regions Meet Masked Autoencoders

Duy-Kien Nguyen · Yanghao Li · Vaibhav Aggarwal · Martin R. Oswald · Alexander Kirillov · Cees G Snoek · Xinlei Chen

Halle B #238
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Wed 8 May 1:45 a.m. PDT — 3:45 a.m. PDT


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

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