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Edge Enhanced Image Style Transfer via Transformers

Authors:

Jun YangAuthors Info & Claims

ICMR '23: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval

Pages 105 - 114

https://doi.org/10.1145/3591106.3592257

Published: 12 June 2023 Publication History

Abstract

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged, and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT (Style Transfer via Transformers) for image style transfer, and an edge loss function that can enhance the content details and avoid generating blurred results due to the excessive rendering of style features. Extensive qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer approaches while alleviating the content leak problem.

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Cited By

Zhou XZheng YYang J(2025)Bridging the metrics gap in image style transfer: A comprehensive survey of models and criteriaNeurocomputing10.1016/j.neucom.2025.129430624(129430)Online publication date: Apr-2025
https://doi.org/10.1016/j.neucom.2025.129430
He DQian WCao J(2025)GEAST-RF: Geometry Enhanced 3D Arbitrary Style Transfer Via Neural Radiance FieldsComputers & Graphics10.1016/j.cag.2025.104181127(104181)Online publication date: Apr-2025
https://doi.org/10.1016/j.cag.2025.104181
You BHonarvar Shakibaei Asli B(2024)A Model Development Approach Based on Point Cloud Reconstruction and Mapping Texture EnhancementBig Data and Cognitive Computing10.3390/bdcc81101648:11(164)Online publication date: 20-Nov-2024
https://doi.org/10.3390/bdcc8110164
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Index Terms

Edge Enhanced Image Style Transfer via Transformers
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
  2. Computer graphics
    1. Rendering
      1. Non-photorealistic rendering

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Published In

cover image ACM Conferences

ICMR '23: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval

June 2023

694 pages

ISBN:9798400701788

DOI:10.1145/3591106

Editors:
Ioannis (Yiannis) Kompatsiaris
Centre for Research and Technology Hellas, Greece
,
Jiebo Luo
University of Rochester,USA
,
Nicu Sebe
University of Trento, Italy
,
Angela Yao
National University of Singapore, Singapore
,
Vasileios Mezaris
Centre for Research and Technology Hellas, Greece
,
Symeon Papadopoulos
Centre for Research and Technology Hellas, Greece
,
Adrian Popescu
CEA LIST, France
,
Zi (Helen) Huang
University of Queensland, Australia

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Published: 12 June 2023

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National Key R&D Program of China
Research Initiation Project of Zhejiang Lab
Natural Science Foundation of Sichuan Province
National Natural Science Foundation of China

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ICMR '23

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SIGMM

ICMR '23: International Conference on Multimedia Retrieval

June 12 - 15, 2023

Thessaloniki, Greece

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Zhou XZheng YYang J(2025)Bridging the metrics gap in image style transfer: A comprehensive survey of models and criteriaNeurocomputing10.1016/j.neucom.2025.129430624(129430)Online publication date: Apr-2025
https://doi.org/10.1016/j.neucom.2025.129430
He DQian WCao J(2025)GEAST-RF: Geometry Enhanced 3D Arbitrary Style Transfer Via Neural Radiance FieldsComputers & Graphics10.1016/j.cag.2025.104181127(104181)Online publication date: Apr-2025
https://doi.org/10.1016/j.cag.2025.104181
You BHonarvar Shakibaei Asli B(2024)A Model Development Approach Based on Point Cloud Reconstruction and Mapping Texture EnhancementBig Data and Cognitive Computing10.3390/bdcc81101648:11(164)Online publication date: 20-Nov-2024
https://doi.org/10.3390/bdcc8110164
Liu MHe SLin SWen BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Dual-head Genre-instance Transformer Network for Arbitrary Style TransferProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681569(6024-6032)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681569
Hao RLi JChen QChen HGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)DualStyle3D: Real-time Exemplar-based Artistic Portrait View Synthesis Based on Radiance FieldProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658019(869-877)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658019
Hsu ZYang STung B(2024) High-Fidelity Image Style Transfer by Hybrid Transformers 1 2024 IEEE International Conference on Visual Communications and Image Processing (VCIP)10.1109/VCIP63160.2024.10849888(1-4)Online publication date: 8-Dec-2024
https://doi.org/10.1109/VCIP63160.2024.10849888
Li HWang LLiu J(2024)A review of deep learning-based image style transfer researchThe Imaging Science Journal10.1080/13682199.2024.2418216(1-23)Online publication date: 23-Oct-2024
https://doi.org/10.1080/13682199.2024.2418216
Shi ZWan W(2024)Transformer-Based adversarial network for semi-supervised face sketch synthesisJournal of Visual Communication and Image Representation10.1016/j.jvcir.2024.104204102(104204)Online publication date: Jun-2024
https://doi.org/10.1016/j.jvcir.2024.104204
Lu DLiu CSun TWang HYin JYang J(2023)X2Ka Translation Network: Mitigating Ka-Band PolSAR Data Insufficiency via Neural Style TransferIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.333487461(1-15)Online publication date: 2023
https://doi.org/10.1109/TGRS.2023.3334874
Cai QMa MWang CLi H(2023)Image neural style transferComputers and Electrical Engineering10.1016/j.compeleceng.2023.108723108:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.compeleceng.2023.108723

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