This paper surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these
approaches into three categories: ($i$) VLP for image-text tasks, such as image
captioning, image-text retrieval, visual question answering, and visual
grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image
classification, object detection, and segmentation; and ($iii$) VLP for
video-text tasks, such as video captioning, video-text retrieval, and video
question answering. For each category, we present a comprehensive review of
state-of-the-art methods, and discuss the progress that has been made and
challenges still being faced, using specific systems and models as case
studies. In addition, for each category, we discuss advanced topics being
actively explored in the research community, such as big foundation models,
unified modeling, in-context few-shot learning, knowledge, robustness, and
computer vision in the wild, to name a few.

本文研究多模态智能领域的视觉 - 语言预训练方法，分为三类分类，包含图像 - 文本、核心计算机视觉和视频 - 文本任务，针对每类任务，提出了针对性的方法，分别探究了研究进展和存在的挑战并讨论了更先进的主题。