  Masked Image Modeling (MIM) achieves outstanding success in self-supervised
representation learning. Unfortunately, MIM models typically have huge
computational burden and slow learning process, which is an inevitable obstacle
for their industrial applications. Although the lower layers play the key role
in MIM, existing MIM models conduct reconstruction task only at the top layer
of encoder. The lower layers are not explicitly guided and the interaction
among their patches is only used for calculating new activations. Considering
the reconstruction task requires non-trivial inter-patch interactions to reason
target signals, we apply it to multiple local layers including lower and upper
layers. Further, since the multiple layers expect to learn the information of
different scales, we design local multi-scale reconstruction, where the lower
and upper layers reconstruct fine-scale and coarse-scale supervision signals
respectively. This design not only accelerates the representation learning
process by explicitly guiding multiple layers, but also facilitates multi-scale
semantical understanding to the input. Extensive experiments show that with
significantly less pre-training burden, our model achieves comparable or better
performance on classification, detection and segmentation tasks than existing
MIM models.
