  Due to the prevalence of scale variance in nature images, we propose to use
image scale as a self-supervised signal for Masked Image Modeling (MIM). Our
method involves selecting random patches from the input image and downsampling
them to a low-resolution format. Our framework utilizes the latest advances in
super-resolution (SR) to design the prediction head, which reconstructs the
input from low-resolution clues and other patches. After 400 epochs of
pre-training, our Super Resolution Masked Autoencoders (SRMAE) get an accuracy
of 82.1% on the ImageNet-1K task. Image scale signal also allows our SRMAE to
capture scale invariance representation. For the very low resolution (VLR)
recognition task, our model achieves the best performance, surpassing DeriveNet
by 1.3%. Our method also achieves an accuracy of 74.84% on the task of
recognizing low-resolution facial expressions, surpassing the current
state-of-the-art FMD by 9.48%.
