The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Arbitrary Style Transfer With Style-Attentional Networks
Open accessAbstract
Arbitrary style transfer aims to synthesize a content image with the style of an image to create a third image that has never been seen before. Recent arbitrary style transfer algorithms find it challenging to balance the content structure and the style patterns. Moreover, simultaneously maintaining the global and local style patterns is difficult due to the patch-based mechanism. In this paper, we introduce a novel style-attentional network (SANet) that efficiently and flexibly integrates the local style patterns according to the semantic spatial distribution of the content image. A new identity loss function and multi-level feature embeddings enable our SANet and decoder to preserve the content structure as much as possible while enriching the style patterns. Experimental results demonstrate that our algorithm synthesizes stylized images in real-time that are higher in quality than those produced by the state-of-the-art algorithms.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Adam: A Method for Stochastic Optimization
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Microsoft COCO: Common Objects in Context
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Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025
Self-Attention Generative Adversarial Networks
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Instance Normalization: The Missing Ingredient for Fast Stylization
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Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
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AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Liu, S., Lin, T., He, D., Li, F., Wang, M., Li, X., Sun, Z., Li, Q., & Ding, E. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00658 · Cited by 420
MarioNETte: Few-Shot Face Reenactment Preserving Identity of Unseen Targets
Ha, S., Kersner, M., Kim, B., Seo, S., & Kim, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6721 · Cited by 157
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
Esser, P., Rombach, R., & Ommer, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00924 · Cited by 72
Artinter: AI-powered Boundary Objects for Commissioning Visual Arts
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Metadata
- Authors
- Park, D. Y., & Lee, K. H.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5873-5881
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2902904812
- Cited by
- 466
- References
- 27
- FWCI
- 10.96
- Type
- conference paper
- Language
- en
- Open access
- green
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