  Masked Image Modeling (MIM) is a new self-supervised vision pre-training
paradigm using a Vision Transformer (ViT). Previous works can be pixel-based or
token-based, using original pixels or discrete visual tokens from parametric
tokenizer models, respectively. Our proposed centroid-based approach, CCViT,
leverages k-means clustering to obtain centroids for image modeling without
supervised training of the tokenizer model, which only takes seconds to create.
This non-parametric centroid tokenizer only takes seconds to create and is
faster for token inference. The centroids can represent both patch pixels and
index tokens with the property of local invariance. Specifically, we adopt
patch masking and centroid replacing strategies to construct corrupted inputs,
and two stacked encoder blocks to predict corrupted patch tokens and
reconstruct original patch pixels. Experiments show that our CCViT achieves
84.4% top-1 accuracy on ImageNet-1K classification with ViT-B and 86.0% with
ViT-L. We also transfer our pre-trained model to other downstream tasks. Our
approach achieves competitive results with recent baselines without external
supervision and distillation training from other models.
