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BEiT: BERT Pre-Training of Image Transformers - OpenReview BEiT relies on a pre-pre-trained tokenizer that transforms image patches into discrete tokens, which are then masked and predicted Extensive experiments show that this self-supervised pre-training improve SoTA in various downstream tasks such as image classification and semantic segmentation
BEIT: RE-TRAINING OF IMAGE TRANSFORMERS - OpenReview We pretrain BEIT and conduct extensive fine-tuning experiments on downstream tasks, such as image classification, and semantic segmentation We present that the self-attention mechanism of self-supervised BEIT learns to distinguish semantic regions and object boundaries, although without using any human annotation
BEIT 2: M IMAGE MODELING WITH V -Q VISUAL TOKENIZERS - OpenReview VQ-KD discretized a continuous semantic space that provides supervision for masked image modeling rather than relying on image pixels The semantic visual tokenizer greatly improved the BEIT pretraining and significantly boosted the transfer performance upon downstream tasks, such as image classification, and semantic segmentation