Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer
Open accessAbstract
Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by performing much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
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Wang and colleagues introduced a residual CNN architecture and a loss network to transfer an input picture's artistic style across multiple scales and dimensions.
“In [160], Wang et al. introduced a residual CNN architecture and loss network to transfer the artistic style of the input picture across multiple scales and dimensions.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [160]
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Identity-Preserving Face Recovery from Portraits
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Texture Preserving Photo Style Transfer Network
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Photo-Realistic Facial Texture Transfer
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Deep attentive style transfer for images with wavelet decomposition
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Location-Free Camouflage Generation Network
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A Simple Way of Multimodal and Arbitrary Style Transfer
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Can Computational Intelligence Model Phenomenal Consciousness?
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Is Bigger Always Better? An Empirical Study on Efficient Architectures for Style Transfer and Beyond
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- Authors
- Wang, X., Oxholm, G., Zhang, D., & Wang, Y.-F.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 7178-7186
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2572559801
- Cited by
- 182
- References
- 35
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 24 · 2023: 20 · 2022: 16 · 2021: 33 · 2020: 25 · 2019: 30 · 2018: 17 · 2017: 4
- FWCI
- 6.08
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- artistic style transfer, multimodal convolutional neural network, texture preservation, offline training, high-resolution imagery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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