CRPGAN: Learning image-to-image translation of two unpaired images by cross-attention mechanism and parallelization strategy Article Swipe
Long Feng
,
Guohua Geng
,
Qihang Li
,
Yi Jiang
,
Zhan Li
,
Kang Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0280073
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0280073
Unsupervised image-to-image translation (UI2I) tasks aim to find a mapping between the source and the target domains from unpaired training data. Previous methods can not effectively capture the differences between the source and the target domain on different scales and often leads to poor quality of the generated images, noise, distortion, and other conditions that do not match human vision perception, and has high time complexity. To address this problem, we propose a multi-scale training structure and a progressive growth generator method to solve UI2I task. Our method refines the generated images from global structures to local details by adding new convolution blocks continuously and shares the network parameters in different scales and also in the same scale of network. Finally, we propose a new Cross-CBAM mechanism (CRCBAM), which uses a multi-layer spatial attention and channel attention cross structure to generate more refined style images. Experiments on our collected Opera Face, and other open datasets Summer↔Winter, Horse↔Zebra, Photo↔Van Gogh, show that the proposed algorithm is superior to other state-of-art algorithms.
Related Topics
Concepts
Computer science
Image translation
Artificial intelligence
Translation (biology)
Image (mathematics)
Convolution (computer science)
Noise (video)
Feature (linguistics)
Pattern recognition (psychology)
Distortion (music)
Face (sociological concept)
Domain (mathematical analysis)
Scale (ratio)
Image quality
Computer vision
Algorithm
Mathematics
Artificial neural network
Chemistry
Gene
Messenger RNA
Computer network
Linguistics
Bandwidth (computing)
Social science
Mathematical analysis
Physics
Sociology
Quantum mechanics
Amplifier
Philosophy
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0280073
- OA Status
- gold
- Cited By
- 6
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313644405
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313644405Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0280073Digital Object Identifier
- Title
-
CRPGAN: Learning image-to-image translation of two unpaired images by cross-attention mechanism and parallelization strategyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-06Full publication date if available
- Authors
-
Long Feng, Guohua Geng, Qihang Li, Yi Jiang, Zhan Li, Kang LiList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0280073Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0280073Direct OA link when available
- Concepts
-
Computer science, Image translation, Artificial intelligence, Translation (biology), Image (mathematics), Convolution (computer science), Noise (video), Feature (linguistics), Pattern recognition (psychology), Distortion (music), Face (sociological concept), Domain (mathematical analysis), Scale (ratio), Image quality, Computer vision, Algorithm, Mathematics, Artificial neural network, Chemistry, Gene, Messenger RNA, Computer network, Linguistics, Bandwidth (computing), Social science, Mathematical analysis, Physics, Sociology, Quantum mechanics, Amplifier, Philosophy, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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