A Survey of Vision-Language Pre-Trained Models

• URL: http://arxiv.org/abs/2202.10936v1
• Date: Fri, 18 Feb 2022 15:15:46 GMT
• Title: A Survey of Vision-Language Pre-Trained Models
• Authors: Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao
• Abstract summary: Pre-trained models have advanced at a breakneck pace in recent years. How to adapt pre-training to the field of Vision-and-Language learning and improve the performance on downstream tasks becomes a focus of multimodal learning.
• Score: 41.323956143107644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Transformer evolved, pre-trained models have advanced at a breakneck pace in recent years. They have dominated the mainstream techniques in natural language processing (NLP) and computer vision (CV). How to adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks becomes a focus of multimodal learning. In this paper, we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs). As the core content, we first briefly introduce several ways to encode raw images and texts to single-modal embeddings before pre-training. Then, we dive into the mainstream architectures of VL-PTMs in modeling the interaction between text and image representations. We further present widely-used pre-training tasks, after which we introduce some common downstream tasks. We finally conclude this paper and present some promising research directions. Our survey aims to provide multimodal researchers a synthesis and pointer to related research.

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