PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization Article Swipe
Junhyeong Cho
,
Gilhyun Nam
,
Sungyeon Kim
,
Hunmin Yang
,
Suha Kwak
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.15199
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.15199
In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Also, a recent study has demonstrated the cross-modal transferability phenomenon of this joint space. From these observations, we propose PromptStyler which simulates various distribution shifts in the joint space by synthesizing diverse styles via prompts without using any images to deal with source-free domain generalization. The proposed method learns to generate a variety of style features (from "a S* style of a") via learnable style word vectors for pseudo-words S*. To ensure that learned styles do not distort content information, we force style-content features (from "a S* style of a [class]") to be located nearby their corresponding content features (from "[class]") in the joint vision-language space. After learning style word vectors, we train a linear classifier using synthesized style-content features. PromptStyler achieves the state of the art on PACS, VLCS, OfficeHome and DomainNet, even though it does not require any images for training.
Related Topics
Concepts
Computer science
Generalization
Style (visual arts)
Artificial intelligence
Joint (building)
Classifier (UML)
Space (punctuation)
Class (philosophy)
Feature vector
Natural language processing
Speech recognition
Pattern recognition (psychology)
Mathematics
Engineering
Archaeology
History
Mathematical analysis
Operating system
Architectural engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.15199
- https://arxiv.org/pdf/2307.15199
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385436612
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385436612Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2307.15199Digital Object Identifier
- Title
-
PromptStyler: Prompt-driven Style Generation for Source-free Domain GeneralizationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-27Full publication date if available
- Authors
-
Junhyeong Cho, Gilhyun Nam, Sungyeon Kim, Hunmin Yang, Suha KwakList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.15199Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.15199Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.15199Direct OA link when available
- Concepts
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Computer science, Generalization, Style (visual arts), Artificial intelligence, Joint (building), Classifier (UML), Space (punctuation), Class (philosophy), Feature vector, Natural language processing, Speech recognition, Pattern recognition (psychology), Mathematics, Engineering, Archaeology, History, Mathematical analysis, Operating system, Architectural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.which | 46 |
| abstract_inverted_index.(e.g., | 8, 22 |
| abstract_inverted_index.domain | 69 |
| abstract_inverted_index.ensure | 97 |
| abstract_inverted_index.images | 64, 166 |
| abstract_inverted_index.learns | 74 |
| abstract_inverted_index.linear | 140 |
| abstract_inverted_index.method | 73 |
| abstract_inverted_index.nearby | 120 |
| abstract_inverted_index.recent | 28 |
| abstract_inverted_index.shifts | 50 |
| abstract_inverted_index.space, | 4 |
| abstract_inverted_index.space. | 39, 131 |
| abstract_inverted_index.styles | 58, 100 |
| abstract_inverted_index.though | 160 |
| abstract_inverted_index.content | 104, 123 |
| abstract_inverted_index.distort | 103 |
| abstract_inverted_index.diverse | 57 |
| abstract_inverted_index.feature | 7 |
| abstract_inverted_index.learned | 99 |
| abstract_inverted_index.located | 119 |
| abstract_inverted_index.prompts | 60 |
| abstract_inverted_index.propose | 44 |
| abstract_inverted_index.require | 164 |
| abstract_inverted_index.variety | 78 |
| abstract_inverted_index.various | 48 |
| abstract_inverted_index.vectors | 92 |
| abstract_inverted_index.without | 61 |
| abstract_inverted_index.achieves | 147 |
| abstract_inverted_index.features | 21, 81, 109, 124 |
| abstract_inverted_index.generate | 76 |
| abstract_inverted_index.learning | 133 |
| abstract_inverted_index.photos). | 25 |
| abstract_inverted_index.proposed | 72 |
| abstract_inverted_index.relevant | 19 |
| abstract_inverted_index.vectors, | 136 |
| abstract_inverted_index.[class]") | 116 |
| abstract_inverted_index.features. | 145 |
| abstract_inverted_index.learnable | 89 |
| abstract_inverted_index.represent | 17 |
| abstract_inverted_index.simulates | 47 |
| abstract_inverted_index.training. | 168 |
| abstract_inverted_index."[class]") | 126 |
| abstract_inverted_index.DomainNet, | 158 |
| abstract_inverted_index.OfficeHome | 156 |
| abstract_inverted_index.classifier | 141 |
| abstract_inverted_index.phenomenon | 35 |
| abstract_inverted_index.cross-modal | 33 |
| abstract_inverted_index.effectively | 16 |
| abstract_inverted_index.source-free | 68 |
| abstract_inverted_index.synthesized | 143 |
| abstract_inverted_index.PromptStyler | 45, 146 |
| abstract_inverted_index.demonstrated | 31 |
| abstract_inverted_index.distribution | 49 |
| abstract_inverted_index.information, | 105 |
| abstract_inverted_index.pseudo-words | 94 |
| abstract_inverted_index.synthesizing | 56 |
| abstract_inverted_index.corresponding | 122 |
| abstract_inverted_index.observations, | 42 |
| abstract_inverted_index.style-content | 108, 144 |
| abstract_inverted_index.generalization. | 70 |
| abstract_inverted_index.transferability | 34 |
| abstract_inverted_index.vision-language | 3, 130 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |