  Most visual recognition studies rely heavily on crowd-labelled data in deep
neural networks (DNNs) training, and they usually train a DNN for each single
visual recognition task, leading to a laborious and time-consuming visual
recognition paradigm. To address the two challenges, Vision-Language Models
(VLMs) have been intensively investigated recently, which learns rich
vision-language correlation from web-scale image-text pairs that are almost
infinitely available on the Internet and enables zero-shot predictions on
various visual recognition tasks with a single VLM. This paper provides a
systematic review of visual language models for various visual recognition
tasks, including: (1) the background that introduces the development of visual
recognition paradigms; (2) the foundations of VLM that summarize the
widely-adopted network architectures, pre-training objectives, and downstream
tasks; (3) the widely-adopted datasets in VLM pre-training and evaluations; (4)
the review and categorization of existing VLM pre-training methods, VLM
transfer learning methods, and VLM knowledge distillation methods; (5) the
benchmarking, analysis and discussion of the reviewed methods; (6) several
research challenges and potential research directions that could be pursued in
the future VLM studies for visual recognition. A project associated with this
survey has been created at https://github.com/jingyi0000/VLM_survey.
