  This paper presents a unified Vision-Language Pre-training (VLP) model. The
model is unified in that (1) it can be fine-tuned for either vision-language
generation (e.g., image captioning) or understanding (e.g., visual question
answering) tasks, and (2) it uses a shared multi-layer transformer network for
both encoding and decoding, which differs from many existing methods where the
encoder and decoder are implemented using separate models. The unified VLP
model is pre-trained on a large amount of image-text pairs using the
unsupervised learning objectives of two tasks: bidirectional and
sequence-to-sequence (seq2seq) masked vision-language prediction. The two tasks
differ solely in what context the prediction conditions on. This is controlled
by utilizing specific self-attention masks for the shared transformer network.
To the best of our knowledge, VLP is the first reported model that achieves
state-of-the-art results on both vision-language generation and understanding
tasks, as disparate as image captioning and visual question answering, across
three challenging benchmark datasets: COCO Captions, Flickr30k Captions, and
VQA 2.0. The code and the pre-trained models are available at
https://github.com/LuoweiZhou/VLP.
