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A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning

文献类型：期刊论文

作者Zhang, Yuxin1,2; Tang, Fan3; Dong, Weiming1,2; Huang, Haibin4; Ma, Chongyang4; Lee, Tong-Yee5; Xu, Changsheng1,2
刊名ACM TRANSACTIONS ON GRAPHICS
出版日期2023-10-01
卷号42期号:5页码:16
ISSN号0730-0301
关键词Arbitrary style transfer contrastive learning style encoding
DOI10.1145/3605548
英文摘要This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential to achieve satisfactory results. Existing approaches based on deep neural networks typically use second-order statistics to generate the output. However, these hand-crafted features computed from a single image cannot leverage style information sufficiently, which leads to artifacts such as local distortions and style inconsistency. To address these issues, we learn style representation directly from a large number of images based on contrastive learning by considering the relationships between specific styles and the holistic style distribution. Specifically, we present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components: a parallel contrastive learning scheme for style representation and transfer, a domain enhancement (DE) module for effective learning of style distribution, and a generative network for style transfer. Qualitative and quantitative evaluations showthe results of our approach are superior to those obtained via state-of-the-art methods. The code is available at https://github.com/ zyxElsa/CAST_pytorch.
资助项目National Key R&D Program of China[2020AAA0106200] ; National Natural Science Foundation of China[61832016] ; National Natural Science Foundation of China[62102162] ; National Natural Science Foundation of China[U20B2070] ; Beijing Natural Science Foundation[L221013] ; National Science and Technology Council[111-2221-E-006-112-MY3]
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:001086833300008
源URL[http://119.78.100.204/handle/2XEOYT63/21095]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Yuxin
作者单位1.Chinese Acad Sci, Inst Automat, MAIS, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd Zhongguancun, Beijing 100190, Peoples R China
4.Kuaishou Technol, 6 Shangdi West Rd, Beijing 100085, Peoples R China
5.Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, 1 Univ Rd, Tainan 701, Taiwan
推荐引用方式
GB/T 7714		 Zhang, Yuxin,Tang, Fan,Dong, Weiming,et al. A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning[J]. ACM TRANSACTIONS ON GRAPHICS,2023,42(5):16.
APA Zhang, Yuxin.,Tang, Fan.,Dong, Weiming.,Huang, Haibin.,Ma, Chongyang.,...&Xu, Changsheng.(2023).A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning.ACM TRANSACTIONS ON GRAPHICS,42(5),16.
MLA Zhang, Yuxin,et al."A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning".ACM TRANSACTIONS ON GRAPHICS 42.5(2023):16.

入库方式： OAI收割

来源：计算技术研究所

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