  Natural language BERTs are trained with language corpus in a self-supervised
manner. Unlike natural language BERTs, vision language BERTs need paired data
to train, which restricts the scale of VL-BERT pretraining. We propose a
self-training approach that allows training VL-BERTs from unlabeled image data.
The proposed method starts with our unified conditional model -- a vision
language BERT model that can perform zero-shot conditional generation. Given
different conditions, the unified conditional model can generate captions,
dense captions, and even questions. We use the labeled image data to train a
teacher model and use the trained model to generate pseudo captions on
unlabeled image data. We then combine the labeled data and pseudo labeled data
to train a student model. The process is iterated by putting the student model
as a new teacher. By using the proposed self-training approach and only 300k
unlabeled extra data, we are able to get competitive or even better
performances compared to the models of similar model size trained with 3
million extra image data.
