  Masked image modeling (MIM) has become a leading self-supervised learning
strategy. MIMs such as Masked Autoencoder (MAE) learn strong representations by
randomly masking input tokens for the encoder to process, with the decoder
reconstructing the masked tokens to the input. However, MIM pre-trained
encoders often exhibit a limited attention span, attributed to MIM's sole focus
on regressing masked tokens only, which may impede the encoder's broader
context learning. To tackle the limitation, we improve MIM by explicitly
incorporating unmasked tokens into the training process. Specifically, our
method enables the encoder to learn from broader context supervision, allowing
unmasked tokens to experience broader contexts while the decoder reconstructs
masked tokens. Thus, the encoded unmasked tokens are equipped with extensive
contextual information, empowering masked tokens to leverage the enhanced
unmasked tokens for MIM. As a result, our simple remedy trains more
discriminative representations revealed by achieving 84.2% top-1 accuracy with
ViT-B on ImageNet-1K with 0.6%p gain. We attribute the success to the enhanced
pre-training method, as evidenced by the singular value spectrum and attention
analyses. Finally, our models achieve significant performance gains at the
downstream semantic segmentation and fine-grained visual classification tasks;
and on diverse robust evaluation metrics. Code is available at
https://github.com/naver-ai/lut
