  We introduce Corrupted Image Modeling (CIM) for self-supervised visual
pre-training. CIM uses an auxiliary generator with a small trainable BEiT to
corrupt the input image instead of using artificial [MASK] tokens, where some
patches are randomly selected and replaced with plausible alternatives sampled
from the BEiT output distribution. Given this corrupted image, an enhancer
network learns to either recover all the original image pixels, or predict
whether each visual token is replaced by a generator sample or not. The
generator and the enhancer are simultaneously trained and synergistically
updated. After pre-training, the enhancer can be used as a high-capacity visual
encoder for downstream tasks. CIM is a general and flexible visual pre-training
framework that is suitable for various network architectures. For the first
time, CIM demonstrates that both ViT and CNN can learn rich visual
representations using a unified, non-Siamese framework. Experimental results
show that our approach achieves compelling results in vision benchmarks, such
as ImageNet classification and ADE20K semantic segmentation.
