Abstract: Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies operate on low-level image pixels, which hinders the exploitation of high-level semantics for representation models. In this work, we propose to use a semantic-rich visual tokenizer as the reconstruction target for masked prediction, providing a systematic way to promote MIM from pixel-level to semantic-level. Specifically, we propose vector-quantized knowledge distillation to train the tokenizer, which discretizes a continuous semantic space to compact codes. We then pretrain vision Transformers by predicting the original visual tokens for the masked image patches. Furthermore, we introduce a patch aggregation strategy which associates discrete image patches to enhance global semantic representation. Experiments on image classification and semantic segmentation show that BEiT v2 outperforms all compared MIM methods. On ImageNet-1K (224 size), the base-size BEiT v2 achieves $85.5\%$ top-1 accuracy for fine-tuning and $80.1\%$ top-1 accuracy for linear probing. The large-size BEiT v2 obtains $87.3\%$ top-1 accuracy for ImageNet-1K (224 size) fine-tuning, and $56.7\%$ mIoU on ADE20K for semantic segmentation. The code can be found in the supplementary materials.
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