[1] 陈淮源, 张广驰, 陈高, 等. 基于深度学习的图像风格迁移研究进展[J]. 计算机工程与应用, 2021, 57(11): 37-45.
CHEN H Y, ZHANG G C, CHEN G, et al. Research progress of image style transfer based on deep learning[J]. Computer Engineering and Applications, 2021, 57(11): 37-45.
[2] HERTZMANN A, JACOBS C E, OLIVER N, et al. Image analogies[C]//The 28th Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 2001: 327-340.
[3] GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE Press, 2016: 2414-2423.
[4] JOHNSON J, ALAHI A, LI F F. Perceptual losses for real-time style transfer and super-resolution[C]//European Conference on Computer Vision, 2016: 694-711.
[5] LIU X C, CHENG M M, LAI Y K, et al. Depth-aware neural style transfer[C]//Proceedings of the Symposium on Non-Photorealistic Animation and Rendering. Los Angeles: IEEE Press, 2017: 1-10.
[6] JING Y, LIU Y, YANG Y, et al. Stroke controllable fast style transfer with adaptive receptive fields[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 238-254.
[7] KOTOVENKO D, SANAKOYEU A, LANG S, et al. Content and style disentanglement for artistic style transfer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul: IEEE Press, 2019: 4422-4431.
[8] CHEN D, YUAN L, LIAO J, et al. Stylebank: an explicit representation for neural image style transfer[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1897-1906.
[9] LI Y, FANG C, YANG J, et al. Diversified texture synthesis with feed-forward networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3920-3928.
[10] LI Y, FANG C, YANG J, et al. Universal style transfer via feature transforms[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 385-395.
[11] HUANG X, BEONGIE S. Arbitrary style transfer in real-time with adaptive instance normalization[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 1501-1510.
[12] JING Y, LIU X, DING Y, et al. Dynamic instance normalization for arbitrary style transfer[C]//Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI, 2020: 4369-4376.
[13] CHEN T Q, SCHMIDT M. Fast patch-based style transfer of arbitrary style[C]//30th Conference on Neural Information Processing Systems, 2016.
[14] PARK D Y, LEE K H. Arbitrary style transfer with style-attentional networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 5880-5888.
[15] LIU S, LIN T, HE D, et al. Adaattn: revisit attenti-on mechanism in arbitrary neural style transfer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 6649-6658.
[16] LUO X, HAN Z, YANG L, et al. Consistent style transfer[EB/OL]. (2022-01-06)[2023-06-12]. https://arxiv.org/abs/2201.02233.
[17] 李鑫, 普园媛, 赵征鹏, 等. 内容语义和风格特征匹配一致的艺术风格迁移[J]. 图学学报, 2023, 44(4): 699-709.
LI X, PU Y Y, ZHAO Z P, et al. Content semantics and style features match consistent artistic style transfer[J]. Journal of Graphics , 2023, 44(4): 699-709.
[18] LI Y, CHEN X, ZHU Z, et al. Attention-guided unified network for panoptic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 7026-7035.
[19] CHIU T Y. Understanding generalized whitening and coloring transform for universal style transfer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 4452-4460.
[20] KARRAS T, LAINE S, AITTALA M, et al. Analyzing and improving the image quality of stylegan[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 8110-8119.
[21] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[EB/OL]. (2014-06-10)[2023-06-12]. https://arxiv.org/abs/1406.2661.
[22] HEUSEL M, RAMSAUER H, UNTERTHINER T, et al. GANs trained by a two time-scale update rule converge to a local nash equilibrium[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6629-6640.
[23] WANG Z, ZHAO L, CHEN H, et al. Evaluate and improve the quality of neural style transfer[J]. Computer Vision and Image Understanding, 2021, 207: 103203. |