Recapturing and rebroadcasting of images are common attack methods in insurance frauds and face identification spoofing, and an increasing number of detection techniques were introduced to handle this problem. However, most of them ignored the domain generalization scenario and scale variances, with an inferior performance on domain shift situations, and normally were exacerbated by intra-domain and inter-domain scale variances. In this paper, we propose a scale alignment domain generalization framework (SADG) to address these challenges. First, an adversarial domain discriminator is exploited to minimize the discrepancies of image representation distributions among different domains. Meanwhile, we exploit triplet loss as a local constraint to achieve a clearer decision boundary. Moreover, a scale alignment loss is introduced as a global relationship regularization to force the image representations of the same class across different scales to be undistinguishable. Experimental results on four databases and comparison with state-of-the-art approaches show that better performance can be achieved using our framework.
Revisiting Invariant Learning for Out-of-Domain Generalization on Multi-Site Mammogram Datasets
Department of Electrical and Computer Engineering, University of Houston（电气与计算机工程系，休斯顿大学）；Department of Systems Medicine and Biomedical Engineering, Houston Methodist Cancer Center（系统医学与生物医学工程系，休斯顿方法主义癌症中心）
Domain-invariant Feature Exploration for Domain Generalization
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Microsoft Research Asia（微软亚洲研究院）；City University of Hong Kong（香港城市大学）；Peng Cheng Laboratory（鹏城实验室）
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization
Sydney AI Centre, The University of Sydney（悉尼大学悉尼人工智能中心）；JD Explore Academy（京东探索研究院）；University of Science and Technology of China（中国科学技术大学）；Nanjing University of Science and Technology（南京理工大学）；Hong Kong Baptist University（香港浸会大学）
Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation
Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）；University of Liverpool（利物浦大学）；AI 3 Fudan University（复旦大学）；Duke Kunshan University（杜克-昆山大学）；Zhongshan Hospital（中山医院）
Class-Invariant Test-Time Augmentation for Domain Generalization
School of Computing and Artificial Intelligence（计算机与人工智能学院）；ANGEL Lab（ANGEL实验室）；Southwest Jiaotong University（西南交通大学）；Nanyang Technological University（南洋理工大学）
Domain Generalization via Conditional Invariant Representation
University of Science and Technology of China（中国科学技术大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
