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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).”
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Research works printed but not linked: 5
- 10.5555/3524938.3525087 · DOI
- Radford, 2018, Improving language understanding by generative pre-training, OpenAI Blog
- Radford, 2019, Language models are unsupervised multitask learners, OpenAI Blog, 1
- 10.1109/cvpr52688.2022.01553 · DOI · Paper page
- Ordonez, Im2Text: Describing images using 1 million captioned photographs, 1143
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Su, J., Luo, Q., Sun, S., Hou, Y., Zhang, X., Heikkilä, J., Yang, C., & Liu, L. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3673339
From graphs to tokens: Substructure-aware molecular representation for large language models
Wang, R., Xing, Z., Liu, X., Yang, M., He, C., & Shen, Y. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104771
Make Geometry Matter for Spatial Reasoning
Zhang, S., Shen, Q., Wang, S., Pan, T., & Wang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37020-4_13
From Pixels to Prompts: A Systematic Study and Introduction to Image Prompt Injection Attacks
Nagaraja, N., Zhang, L., & Wang, Z. (2026) · Computer · doi:10.1109/mc.2025.3633104
Look2React: Making VR NPCs Come Alive with Dynamic Vision-Guided Reactions
Vatsal, R., Huang, X., & Xiao, R. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3679121
Semantic Compensation and Localization for Color Vision Deficiency
Chen, Z., Zhu, Z., & Mao, X. (2025) · International Conference on Cyberworlds (CW) · doi:10.1109/cw68232.2025.00068
Radiologist‑vision‑language model collaboration for breast cancer diagnosis in mammography
Zhou, Z., Arefan, D., Sumkin, J. H., & Wu, S. (2026) · SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment · doi:10.1117/12.3087760
A Comparative Study of Object and Sequence Diagrams in Automated UML Understanding
Nguyen, V.-V., Bui, A.-T., Nguyen, H.-K., Nguyen, K.-S., Luong, T. M.-H., Vu, D.-Q., Nguyen, T.-V., & Bui, Q.-A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21631-1_28
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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