FST-OAM: a fast style transfer model using optimized self-attention mechanism
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03064-w.pdf
Reference53 articles.
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2. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019). https://doi.org/10.1109/CVPR.2019.00453
3. Wang, Z., Zhao, L., Chen, H., et al.: Diversified arbitrary style transfer via deep feature perturbation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7789–7798 (2020). https://doi.org/10.1109/CVPR42600.2020.00781
4. An, J., Huang, S., Song, Y., et al.: Artflow: unbiased image style transfer via reversible neural flows. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 862–871 (2021). https://doi.org/10.1109/CVPR46437.2021.00092
5. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1501–1510 (2017). https://doi.org/10.1109/ICCV.2017.167
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