Vision-and-Language Pretrained Models: A Survey
It synthesizes existing knowledge for researchers in computer vision and natural language processing, offering guidance but is incremental as a survey.
This survey paper provides an overview of vision-and-language pretrained models (VLPMs), summarizing advances in joint representation learning, data encoding methods, model structures, and training strategies, while highlighting future directions for researchers.
Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP). This progress leads to learning joint representations of vision and language pretraining by feeding visual and linguistic contents into a multi-layer transformer, Visual-Language Pretrained Models (VLPMs). In this paper, we present an overview of the major advances achieved in VLPMs for producing joint representations of vision and language. As the preliminaries, we briefly describe the general task definition and genetic architecture of VLPMs. We first discuss the language and vision data encoding methods and then present the mainstream VLPM structure as the core content. We further summarise several essential pretraining and fine-tuning strategies. Finally, we highlight three future directions for both CV and NLP researchers to provide insightful guidance.