Masked Autoencoder (MAE) has demonstrated superior performance on various
vision tasks via randomly masking image patches and reconstruction. However,
effective data augmentation strategies for MAE still remain open questions,
different from those in contrastive learning that serve as the most important
part. This paper studies the prevailing mixing augmentation for MAE. We first
demonstrate that naive mixing will in contrast degenerate model performance due
to the increase of mutual information (MI). To address, we propose homologous
recognition, an auxiliary pretext task, not only to alleviate the MI
increasement by explicitly requiring each patch to recognize homologous
patches, but also to perform object-aware self-supervised pre-training for
better downstream dense perception performance. With extensive experiments, we
demonstrate that our proposed Mixed Autoencoder (MixedAE) achieves the
state-of-the-art transfer results among masked image modeling (MIM)
augmentations on different downstream tasks with significant efficiency.
Specifically, our MixedAE outperforms MAE by +0.3% accuracy, +1.7 mIoU and +0.9
AP on ImageNet-1K, ADE20K and COCO respectively with a standard ViT-Base.
Moreover, MixedAE surpasses iBOT, a strong MIM method combined with instance
discrimination, while accelerating training by 2x. To our best knowledge, this
is the very first work to consider mixing for MIM from the perspective of
pretext task design. Code will be made available.

本文研究了 Masked Autoencoder 的数据扩增问题，提出了基于同源识别的 Mix Autoencoder 方法，通过自监督预训练可以提升下游视觉任务表现，在 ImageNet、ADE20K 和 COCO 数据集上 outperforms 了 MAE 和 iBOT 的方法。