  The self-supervised Masked Image Modeling (MIM) schema, following
"mask-and-reconstruct" pipeline of recovering contents from masked image, has
recently captured the increasing interest in the multimedia community, owing to
the excellent ability of learning visual representation from unlabeled data.
Aiming at learning representations with high semantics abstracted, a group of
works attempts to reconstruct non-semantic pixels with large-ratio masking
strategy, which may suffer from "over-smoothing" problem, while others directly
infuse semantics into targets in off-line way requiring extra data. Different
from them, we shift the perspective to the Fourier domain which naturally has
global perspective and present a new Masked Image Modeling (MIM), termed
Geminated Gestalt Autoencoder (Ge$^2$-AE) for visual pre-training.
Specifically, we equip our model with geminated decoders in charge of
reconstructing image contents from both pixel and frequency space, where each
other serves as not only the complementation but also the reciprocal
constraints. Through this way, more robust representations can be learned in
the pre-trained encoders, of which the effectiveness is confirmed by the
juxtaposing experimental results on downstream recognition tasks. We also
conduct several quantitative and qualitative experiments to investigate the
learning behavior of our method. To our best knowledge, this is the first MIM
work to solve the visual pre-training through the lens of frequency domain.
