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

Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning