The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Controlling Perceptual Factors in Neural Style Transfer
Abstract
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer.
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Arbitrary Style Transfer With Style-Attentional Networks
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Transfer adaptation learning: A decade survey
Zhang, L., & Gao, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183326 · Cited by 314
Oh, C., Song, J., Choi, J., Kim, S., Lee, S., & Suh, B. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174223 · Cited by 301
Metadata
- Authors
- Gatys, L. A., Ecker, A. S., Bethge, M., Hertzmann, A., & Shechtman, E.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3730-3738
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2550673061
- Cited by
- 486
- References
- 24
- FWCI
- 16.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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