This paper presents a comprehensive survey of vision-language (VL) intelligence from the perspective of time. This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension. We summarize the development in this field into three time periods, namely task-specific methods, vision-language pre-training (VLP) methods, and larger models empowered by large-scale weakly-labeled data. We first take some common VL tasks as examples to introduce the development of task-specific methods. Then we focus on VLP methods and comprehensively review key components of the model structures and training methods. After that, we show how recent work utilizes large-scale raw image-text data to learn language-aligned visual representations that generalize better on zero or few shot learning tasks. Finally, we discuss some potential future trends towards modality cooperation, unified representation, and knowledge incorporation. We believe that this review will be of help for researchers and practitioners of AI and ML, especially those interested in computer vision and natural language processing.
@article{arxiv.2203.01922,
title = {Vision-Language Intelligence: Tasks, Representation Learning, and Large Models},
author = {Feng Li and Hao Zhang and Yi-Fan Zhang and Shilong Liu and Jian Guo and Lionel M. Ni and PengChuan Zhang and Lei Zhang},
journal= {arXiv preprint arXiv:2203.01922},
year = {2022}
}