Masked Unsupervised Self-training for Zero-shot Image Classification

@article{Li2022MaskedUS,
  title={Masked Unsupervised Self-training for Zero-shot Image Classification},
  author={Junnan Li and Silvio Savarese and Steven C. H. Hoi},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.02967}
}
State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classification tasks. However… 

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