In this paper, we study how to use masked signal modeling in vision and
language (V+L) representation learning. Instead of developing masked language
modeling (MLM) and masked image modeling (MIM) independently, we propose to
build joint masked vision and language modeling, where the masked signal of one
modality is reconstructed with the help from another modality. This is
motivated by the nature of image-text paired data that both of the image and
the text convey almost the same information but in different formats. The
masked signal reconstruction of one modality conditioned on another modality
can also implicitly learn cross-modal alignment between language tokens and
image patches. Our experiments on various V+L tasks show that the proposed
method, along with common V+L alignment losses, achieves state-of-the-art
performance in the regime of millions of pre-training data. Also, we
outperforms the other competitors by a significant margin in limited data
scenarios.

本文研究如何使用掩码信号建模来实现视觉和语言（V + L）表示学习，提出了联合掩码视觉和语言建模的方法，通过不同的模态互相重构，隐式地学习语言标记和图像补丁的交叉模态对齐，并在各种 V + L 任务中实现了最先进的性能。