Visual Attribute Transfer through Deep Image Analogy Article Swipe
Jing Liao
,
Yuan Yao
,
Lu Yuan
,
Gang Hua
,
Sing Bing Kang
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.01088
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.01088
We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene. Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of "image analogy" with features extracted from a Deep Convolutional Neutral Network for matching; we call our technique Deep Image Analogy. A coarse-to-fine strategy is used to compute the nearest-neighbor field for generating the results. We validate the effectiveness of our proposed method in a variety of cases, including style/texture transfer, color/style swap, sketch/painting to photo, and time lapse.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1705.01088
- https://arxiv.org/pdf/1705.01088
- OA Status
- green
- Cited By
- 32
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2951924128
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https://openalex.org/W2951924128Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1705.01088Digital Object Identifier
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Visual Attribute Transfer through Deep Image AnalogyWork title
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preprintOpenAlex work type
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enPrimary language
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2017Year of publication
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2017-05-02Full publication date if available
- Authors
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Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, Sing Bing KangList of authors in order
- Landing page
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https://arxiv.org/abs/1705.01088Publisher landing page
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https://arxiv.org/pdf/1705.01088Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1705.01088Direct OA link when available
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Analogy, Artificial intelligence, Sketch, Computer science, Convolutional neural network, Computer vision, Image (mathematics), Pattern recognition (psychology), Algorithm, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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32Total citation count in OpenAlex
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2022: 1, 2021: 4, 2020: 7, 2019: 11, 2018: 9Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.painting | 54 |
| abstract_inverted_index.proposed | 134 |
| abstract_inverted_index.results. | 127 |
| abstract_inverted_index.semantic | 21 |
| abstract_inverted_index.strategy | 116 |
| abstract_inverted_index.texture, | 37 |
| abstract_inverted_index.transfer | 8, 29 |
| abstract_inverted_index.validate | 129 |
| abstract_inverted_index.attribute | 7, 25 |
| abstract_inverted_index.different | 15 |
| abstract_inverted_index.extracted | 98 |
| abstract_inverted_index.including | 141 |
| abstract_inverted_index.matching; | 106 |
| abstract_inverted_index.technique | 4, 77, 110 |
| abstract_inverted_index.transfer, | 26, 143 |
| abstract_inverted_index.accomplish | 87 |
| abstract_inverted_index.appearance | 16 |
| abstract_inverted_index.generating | 125 |
| abstract_inverted_index.structure. | 22 |
| abstract_inverted_index.color/style | 144 |
| abstract_inverted_index.information | 32 |
| abstract_inverted_index.perceptually | 19 |
| abstract_inverted_index.Convolutional | 102 |
| abstract_inverted_index.effectiveness | 131 |
| abstract_inverted_index.style/texture | 142 |
| abstract_inverted_index.coarse-to-fine | 115 |
| abstract_inverted_index.correspondences | 81 |
| abstract_inverted_index.sketch/painting | 146 |
| abstract_inverted_index.nearest-neighbor | 122 |
| abstract_inverted_index.semantically-meaningful | 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |