mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography
Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院医学影像技术研究所）
Sheng Wang, Jiayu Huo, Xi Ouyang, Jifei Che, Xuhua Ren, Zhong Xue, Qian Wang, Jie-Zhi Cheng
Computer-aided diagnosis with deep learning techniques has been shown to be helpful for the diagnosis of the mammography in many clinical studies. However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model. In this study, we explicitly address style variety issue with the proposed multi-resolution and multi-reference neural style transfer (mr2NST) network. The mr2NST can normalize the styles from different vendors to the same style baseline with very high resolution. We illustrate that the image quality of the transferred images is comparable to the quality of original images of the target domain (vendor) in terms of NIMA scores. Meanwhile, the mr2NST results are also shown to be helpful for the lesion detection in mammograms.
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Jiao Tong University（上海交通大学）；The University of Hong Kong（香港大学）；Yale University（耶鲁大学）；ShanghaiTech University（上海科技大学）
风格转移的目的是从参考图像再现具有风格的内容图像。现有的通用风格转移方法成功地提供任意风格的原始图像，无论是在艺术或照片逼真的方式。然而，现有的作品所定义的“任意风格”的范围是有限的，在特定的领域，由于其结构的限制。具体地，根据预定义的目标域来建立内容保存和风格化的程度。因此，照片级真实感和艺术级模型都难以为另一个域执行所需的风格转换。为了克服这一限制，我们提出了一个统一的架构，域感知风格传输网络（Domain-aware Style Transfer Networks，DSTN），它不仅传输风格，而且传输域的属性（即，域性）。为此，我们设计了一种新的域指示器，从参考图像的纹理和结构特征中捕获域值。此外，我们引入了一个统一的框架与域感知跳过连接自适应地转移的笔画和调色板的输入内容的域指示器的指导下。我们广泛的实验验证了我们的模型产生更好的定性结果，并优于以前的方法在艺术和照片逼真的风格化的代理指标。
Training-Free Multi-Style Fusion Through Reference-Based Adaptive Modulation
University of Washington, Seattle, WA 98195, USA（华盛顿大学）；Nanjing University of Posts and Telecommunications, Jiangsu, Nanjing, China（南京邮电大学）；Nanjing Foreign Language School, Jiangsu, Nanjing, China（南京外国语学校）
InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style Adviser
Beijing University of Posts and Telecommunications（北京邮电大学）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Style-Preserving Lip Sync via Audio-Aware Style Reference
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Shandong Inspur Database Technology Co., Ltd.（山东 Inspur 数据库技术有限公司）；Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
Only-Style: Stylistic Consistency in Image Generation without Content Leakage
School of Electrical & Computer Engineering, National Technical University of Athens（电气与计算机工程学院，国家技术大学雅典分校）；Robotics Institute, Athena Research Center（机器人研究所，雅典研究中心）；HERON - Center of Excellence in Robotics（HERON-机器人卓越中心）
Styleverse: Towards Identity Stylization across Heterogeneous Domains
National Laboratory of Pattern Recognition, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；Center for Excellence in Brain Science and Intelligence Technology, CAS（中国科学院脑科学与智能技术卓越创新中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
StyleAdapter: A Unified Stylized Image Generation Model
The University of Hong Kong（香港大学）；ARC Lab, Tencent PCG（腾讯平台与内容事业群ARC实验室）；The University of Macau（澳门大学）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）
LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation
GuangDong Province Key Lab of Information Security Technology and School of Computer Science and Engineering（广东信息安全技术重点实验室和计算机科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus（深圳校区网络科学与技术学院）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering（广东机器感知与智能计算实验室，工程学院）
HAM: A Training-Free Style Transfer Approach via Heterogeneous Attention Modulation for Diffusion Models
Hangzhou Dianzi University（杭州电子科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Macao Polytechnic University（澳门 polytechnic 大学）
StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements
Westlake University（西湖大学）；Fudan University（复旦大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
