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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
Context Autoencoder for Self-Supervised Representation Learning Metareview: Summary, Strengths And Weaknesses: The paper proposes the Contextual Autoencoder (CAE) for self-supervised learning It builds on MAE and BEiT to perform masked image modeling with Vision Transformers (ViTs)
Corrupted Image Modeling for Self-Supervised Visual Pre-Training Abstract: We introduce Corrupted Image Modeling (CIM) for self-supervised visual pre-training CIM uses an auxiliary generator with a small trainable BEiT to corrupt the input image instead of using artificial [MASK] tokens, where some patches are randomly selected and replaced with plausible alternatives sampled from the BEiT output distribution Given this corrupted image, an enhancer