  Transformers and masked language modeling are quickly being adopted and
explored in computer vision as vision transformers and masked image modeling
(MIM). In this work, we argue that image token masking differs from token
masking in text, due to the amount and correlation of tokens in an image. In
particular, to generate a challenging pretext task for MIM, we advocate a shift
from random masking to informed masking. We develop and exhibit this idea in
the context of distillation-based MIM, where a teacher transformer encoder
generates an attention map, which we use to guide masking for the student. We
thus introduce a novel masking strategy, called attention-guided masking
(AttMask), and we demonstrate its effectiveness over random masking for dense
distillation-based MIM as well as plain distillation-based self-supervised
learning on classification tokens. We confirm that AttMask accelerates the
learning process and improves the performance on a variety of downstream tasks.
We provide the implementation code at https://github.com/gkakogeorgiou/attmask.
