  Cross-modal alignment is essential for vision-language pre-training (VLP)
models to learn the correct corresponding information across different
modalities. For this purpose, inspired by the success of masked language
modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling
tasks have been proposed for VLP to further promote cross-modal interactions.
The core idea of previous masked modeling tasks is to focus on reconstructing
the masked tokens based on visible context for learning local-to-local
alignment. However, most of them pay little attention to the global semantic
features generated for the masked data, resulting in a limited cross-modal
alignment ability of global representations. Therefore, in this paper, we
propose a novel Semantic Completion Learning (SCL) task, complementary to
existing masked modeling tasks, to facilitate global-to-local alignment.
Specifically, the SCL task complements the missing semantics of masked data by
capturing the corresponding information from the other modality, promoting
learning more representative global features which have a great impact on the
performance of downstream tasks. Moreover, we present a flexible vision
encoder, which enables our model to perform image-text and video-text
multimodal tasks simultaneously. Experimental results show that our proposed
method obtains state-of-the-art performance on various vision-language
benchmarks, such as visual question answering, image-text retrieval, and
video-text retrieval.
