• Corpus ID: 245131381

Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text

@article{Li2021TowardsAU,
  title={Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text},
  author={Qing Li and Boqing Gong and Yin Cui and D. Kondratyuk and Xianzhi Du and Ming-Hsuan Yang and Matthew Brown},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.07074}
}
In this paper, we explore the possibility of building a unified foundation model that can be adapted to both visiononly and text-only tasks. Starting from BERT and ViT, we design a unified transformer consisting of modality-specific tokenizers, a shared transformer encoder, and task-specific output heads. To efficiently pre-train the proposed model jointly on unpaired images and text, we propose two novel techniques: (i) We employ the separately-trained BERT and ViT models as teachers and apply… 

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