  This paper explores a better prediction target for BERT pre-training of
vision transformers. We observe that current prediction targets disagree with
human perception judgment.This contradiction motivates us to learn a perceptual
prediction target. We argue that perceptually similar images should stay close
to each other in the prediction target space. We surprisingly find one simple
yet effective idea: enforcing perceptual similarity during the dVAE training.
Moreover, we adopt a self-supervised transformer model for deep feature
extraction and show that it works well for calculating perceptual similarity.We
demonstrate that such learned visual tokens indeed exhibit better semantic
meanings, and help pre-training achieve superior transfer performance in
various downstream tasks. For example, we achieve $\textbf{84.5\%}$ Top-1
accuracy on ImageNet-1K with ViT-B backbone, outperforming the competitive
method BEiT by $\textbf{+1.3\%}$ under the same pre-training epochs. Our
approach also gets significant improvement on object detection and segmentation
on COCO and semantic segmentation on ADE20K. Equipped with a larger backbone
ViT-H, we achieve the state-of-the-art ImageNet accuracy (\textbf{88.3\%})
among methods using only ImageNet-1K data.
