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Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers
To alleviate this issue, inspired by masked autoencoder (MAE), which is a data-efficient self-supervised learner, we propose Semi-MAE, a pure ViT-based SSL framework consisting of a parallel MAE branch to assist the visual representation learning and make the pseudo labels more accurate.
Accurate Word Representations with Universal Visual Guidance
Word representation is a fundamental component in neural language understanding models.
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