Vision-Language Models for Vision Tasks: A Survey

• URL: http://arxiv.org/abs/2304.00685v2
• Date: Fri, 16 Feb 2024 10:28:12 GMT
• Title: Vision-Language Models for Vision Tasks: A Survey
• Authors: Jingyi Zhang, Jiaxing Huang, Sheng Jin and Shijian Lu
• Abstract summary: Vision-Language Models (VLMs) learn rich vision-language correlation from web-scale image-text pairs. This paper provides a systematic review of visual language models for various visual recognition tasks.
• Score: 62.543250338410836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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