Visual Tuning Article Swipe
Bruce X. B. Yu
,
Jianlong Chang
,
Haixin Wang
,
Lingbo Liu
,
Shijie Wang
,
Zhiyu Wang
,
Junfan Lin
,
Lingxi Xie
,
Haojie Li
,
Zhouchen Lin
,
Qi Tian
,
Chang Wen Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3657632
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3657632
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: fine-tuning, prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1145/3657632
- https://dl.acm.org/doi/pdf/10.1145/3657632
- OA Status
- bronze
- Cited By
- 19
- References
- 102
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394766673
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394766673Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3657632Digital Object Identifier
- Title
-
Visual TuningWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-12Full publication date if available
- Authors
-
Bruce X. B. Yu, Jianlong Chang, Haixin Wang, Lingbo Liu, Shijie Wang, Zhiyu Wang, Junfan Lin, Lingxi Xie, Haojie Li, Zhouchen Lin, Qi Tian, Chang Wen ChenList of authors in order
- Landing page
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https://doi.org/10.1145/3657632Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3657632Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://dl.acm.org/doi/pdf/10.1145/3657632Direct OA link when available
- Concepts
-
Computer science, Fine-tuning, Adapter (computing), Artificial intelligence, Reuse, Selection (genetic algorithm), Human–computer interaction, Machine learning, Computer hardware, Physics, Quantum mechanics, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11, 2024: 8Per-year citation counts (last 5 years)
- References (count)
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102Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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