Cited by 1,051Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

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Vision-Language Models for Vision Tasks: A Survey

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Zhang, Jingyi, Huang, Jiaxing, Jin, Sheng, Lu, Shijian·2024·IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI 10.1109/tpami.2024.3369699

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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.

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Authors: Zhang, J., Huang, J., Jin, S., & Lu, S.
Affiliations: Nanyang Technological University (SG)
ORCID: Zhang, Jingyi
Huang, Jiaxing
Jin, Sheng
Lu, Shijian
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024-08
Volume: 46
Issue: 8
Pages: 5625-5644
ISSN: 0162-8828 (ISSN-L)
2160-9292
1939-3539
DOI: 10.1109/tpami.2024.3369699
OpenAlex: W4392172801
Cited by: 1,051
References: 202
Citations by year: 2026: 432 · 2025: 486 · 2024: 124
FWCI: 132.68
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
DR-NTU (Nanyang Technological University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: vision-language models, visual recognition, zero-shot learning, transfer learning, knowledge distillation, image-text dataset, pre-training objectives, network architecture, benchmarking

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Vision-Language Models for Vision Tasks: A Survey

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