The DOI matches its Crossref record; 194 of the 202 references it cites were found in a registry or an index.
Vision-Language Models for Vision Tasks: A Survey
Open accessAbstract
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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“With the rise of multi-modal forgeries, recent approaches leverage deep learning models such as transformers [8] and visionlanguage models [9] to improve detection capabilities.”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [9]
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“As LLMs expand in the kinds of stimuli they can interpret -e.g.visual (OpenAI, 2023b;Zhang, Huang, et al., 2024;Gemini Team et al., 2024) -and the ways in which they can interact with the world -e.g.embodiment and tool use (Mialon et al., 2023) -, the space of transferable paradigms increases.”
From Machine Psychology (2023) · cites this paper as Zhang, Huang, et al., 2024
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“Regarding improvements in symbolic or numeric reasoning, another technique is to prompt LLMs to use code for solving tasks (Zhang, Ge, et al., 2024).”
From Machine Psychology (2023) · cites this paper as Zhang, Ge, et al., 2024
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Cites
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- Radford, 2018, Improving language understanding by generative pre-training, OpenAI Blog
- Radford, 2019, Language models are unsupervised multitask learners, OpenAI Blog, 1
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Mutis, I., Binana, E., Mehta, R., & Dulanto, P. (2026) · Journal of structural design and construction practice. · doi:10.1061/jsdccc.sceng-2205
A Review of Deep Learning Models and Explainable Artificial Intelligence for Pathology Recognition
Toala-Paz, A., Becerra-Andrade, A., Cevallos-Levicek, D., Peláez, E., Domínguez, F., & Loayza, F. R. (2026) · International Conference on eDemocracy & eGovernment · doi:10.1109/icedeg70169.2026.11695396
Metadata
- Authors
- Zhang, J., Huang, J., Jin, S., & Lu, S.
- Affiliations
- Nanyang Technological University (SG)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2024-08
- Volume
- 46
- Issue
- 8
- Pages
- 5625-5644
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- 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
- 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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