Abstract
State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifacts resulting from patch mismatching. In this paper, we study a novel semi-parametric neural style transfer framework that alleviates the deficiency of both parametric and non-parametric stylization. The core idea of our approach is to establish accurate and fine-grained content-style correspondences using graph neural networks (GNNs). To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices. The style transfer procedure is then modeled as the attention-based heterogeneous message passing between the style and content nodes in a learnable manner, leading to adaptive many-to-one style-content correlations at the local patch level. In addition, an elaborated deformable graph convolutional operation is introduced for cross-scale style-content matching. Experimental results demonstrate that the proposed semi-parametric image stylization approach yields encouraging results on the challenging style patterns, preserving both global appearance and exquisite details. Furthermore, by controlling the number of edges at the inference stage, the proposed method also triggers novel functionalities like diversified patch-based stylization with a single model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
An, J., Huang, S., Song, Y., Dou, D., Liu, W., Luo, J.: ArtFlow: unbiased image style transfer via reversible neural flows. In: CVPR (2021)
Champandard, A.J.: Semantic style transfer and turning two-bit doodles into fine artworks. arXiv preprint arXiv:1603.01768 (2016)
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: StyleBank: an explicit representation for neural image style transfer. In: CVPR (2017)
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Explicit filterbank learning for neural image style transfer and image processing. TPAMI 43, 2373–2387 (2020)
Chen, H., et al.: Diverse image style transfer via invertible cross-space mapping. In: ICCV (2021)
Chen, T.Q., Schmidt, M.: Fast patch-based style transfer of arbitrary style. In: NeurIPS Workshop on Constructive Machine Learning (2016)
Chen, Z., et al.: DPT: deformable patch-based transformer for visual recognition. In: ACM MM (2021)
Ding, L., Wang, L., Liu, X., Wong, D.F., Tao, D., Tu, Z.: Understanding and improving lexical choice in non-autoregressive translation. In: ICLR (2021)
Ding, L., Wang, L., Tao, D.: Self-attention with cross-lingual position representation. In: ACL (2020)
Ding, L., Wang, L., Wu, D., Tao, D., Tu, Z.: Context-aware cross-attention for non-autoregressive translation. In: COLING (2020)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR (2016)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS (2017)
Hong, K., Jeon, S., Yang, H., Fu, J., Byun, H.: Domain-aware universal style transfer. In: ICCV (2021)
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV (2017)
Huo, J., et al.: Manifold alignment for semantically aligned style transfer. In: ICCV (2021)
Jing, Y., et al.: Dynamic instance normalization for arbitrary style transfer. In: AAAI (2020)
Jing, Y., et al.: Stroke controllable fast style transfer with adaptive receptive fields. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 244–260. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_15
Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural style transfer: a review. TVCG 26, 3365–3385 (2019)
Jing, Y., Yang, Y., Wang, X., Song, M., Tao, D.: Amalgamating knowledge from heterogeneous graph neural networks. In: CVPR (2021)
Jing, Y., Yang, Y., Wang, X., Song, M., Tao, D.: Meta-aggregator: learning to aggregate for 1-bit graph neural networks. In: ICCV (2021)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Kalischek, N., Wegner, J.D., Schindler, K.: In the light of feature distributions: moment matching for neural style transfer. In: CVPR (2021)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Kolkin, N., Salavon, J., Shakhnarovich, G.: Style transfer by relaxed optimal transport and self-similarity. In: CVPR (2019)
Kong, Y., Liu, L., Wang, J., Tao, D.: Adaptive curriculum learning. In: ICCV (2021)
Li, C., Wand, M.: Combining Markov random fields and convolutional neural networks for image synthesis. In: CVPR, pp. 2479–2486 (2016)
Li, Y., Wang, N., Liu, J., Hou, X.: Demystifying neural style transfer. In: IJCAI (2017)
Li, Y., Chen, F., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Diversified texture synthesis with feed-forward networks. In: CVPR (2017)
Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Universal style transfer via feature transforms. In: NeurIPS (2017)
Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual attribute transfer through deep image analogy. TOG 36, 1–15 (2017)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, H., Yang, Y., Wang, X.: Overcoming catastrophic forgetting in graph neural networks. In: AAAI (2021)
Liu, S., et al.: Paint transformer: feed forward neural painting with stroke prediction. In: ICCV (2021)
Liu, S., et al.: AdaAttN: revisit attention mechanism in arbitrary neural style transfer. In: ICCV (2021)
Liu, X.C., Yang, Y.L., Hall, P.: Learning to warp for style transfer. In: CVPR (2021)
Mechrez, R., Talmi, I., Zelnik-Manor, L.: The contextual loss for image transformation with non-aligned data. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 800–815. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_47
Nichol, K.: Painter by numbers (2016). https://www.kaggle.com/c/painter-by-numbers
Ren, S., Zhou, D., He, S., Feng, J., Wang, X.: Shunted self-attention via multi-scale token aggregation. In: CVPR (2022)
Risser, E., Wilmot, P., Barnes, C.: Stable and controllable neural texture synthesis and style transfer using histogram losses. arXiv preprint arXiv:1701.08893 (2017)
Shen, C., Yin, Y., Wang, X., Li, X., Song, J., Song, M.: Training generative adversarial networks in one stage. In: CVPR (2021)
Sheng, L., Shao, J., Lin, Z., Warfield, S., Wang, X.: Avatar-Net: multi-scale zero-shot style transfer by feature decoration. In: CVPR (2018)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)
Wang, M., et al.: Deep graph library: towards efficient and scalable deep learning on graphs. In: ICLR Workshop (2019)
Wang, P., Li, Y., Vasconcelos, N.: Rethinking and improving the robustness of image style transfer. In: CVPR (2021)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. TOG 38, 1–12 (2019)
Wu, X., Hu, Z., Sheng, L., Xu, D.: StyleFormer: real-time arbitrary style transfer via parametric style composition. In: ICCV (2021)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2019)
Xu, W., Long, C., Wang, R., Wang, G.: DRB-GAN: a dynamic ResBlock generative adversarial network for artistic style transfer. In: ICCV (2021)
Xu, Y., Zhang, Q., Zhang, J., Tao, D.: ViTAE: vision transformer advanced by exploring intrinsic inductive bias. In: NeurIPS (2021)
Yang, Y., Feng, Z., Song, M., Wang, X.: Factorizable graph convolutional networks. In: NeurIPS (2020)
Yang, Y., Qiu, J., Song, M., Tao, D., Wang, X.: Distilling knowledge from graph convolutional networks. In: CVPR (2020)
Yang, Y., Ren, Z., Li, H., Zhou, C., Wang, X., Hua, G.: Learning dynamics via graph neural networks for human pose estimation and tracking. In: CVPR (2021)
Yang, Y., Wang, X., Song, M., Yuan, J., Tao, D.: SPAGAN: shortest path graph attention network. In: IJCAI (2019)
Ye, J., Jing, Y., Wang, X., Ou, K., Tao, D., Song, M.: Edge-sensitive human cutout with hierarchical granularity and loopy matting guidance. TIP 29, 1177–1191 (2019)
Yu, W., et al.: MetaFormer is actually what you need for vision. In: CVPR (2022)
Zhan, Y., Yu, J., Yu, T., Tao, D.: On exploring undetermined relationships for visual relationship detection. In: CVPR (2019)
Zhan, Y., Yu, J., Yu, T., Tao, D.: Multi-task compositional network for visual relationship detection. IJCV 128, 2146–2165 (2020)
Zhang, H., Dana, K.: Multi-style generative network for real-time transfer. arXiv preprint arXiv:1703.06953 (2017)
Zhang, Q., Xu, Y., Zhang, J., Tao, D.: ViTAEv2: vision transformer advanced by exploring inductive bias for image recognition and beyond. arXiv preprint arXiv:2202.10108 (2022)
Zhang, Q., Xu, Y., Zhang, J., Tao, D.: VSA: learning varied-size window attention in vision transformers. arXiv preprint arXiv:2204.08446 (2022)
Zhao, H., Bian, W., Yuan, B., Tao, D.: Collaborative learning of depth estimation, visual odometry and camera relocalization from monocular videos. In: IJCAI (2020)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. arXiv preprint arXiv:1812.08434 (2018)
Zhou, S., Zhang, J., Zuo, W., Loy, C.C.: Cross-scale internal graph neural network for image super-resolution. In: NeurIPS (2020)
Acknowledgments
Mr Yongcheng Jing is supported by ARC FL-170100117. Dr Xinchao Wang is supported by AI Singapore (Award No.: AISG2-RP-2021-023) and NUS Faculty Research Committee Grant (WBS: A-0009440-00-00).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jing, Y. et al. (2022). Learning Graph Neural Networks for Image Style Transfer. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-20071-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20070-0
Online ISBN: 978-3-031-20071-7
eBook Packages: Computer ScienceComputer Science (R0)