With various face presentation attacks arising under unseen scenarios, face anti-spoofing (FAS) based on domain generalization (DG) has drawn growing attention due to its robustness. Most existing methods utilize DG frameworks to align the features to seek a compact and generalized feature space. However, little attention has been paid to the feature extraction process for the FAS task, especially the influence of normalization, which also has a great impact on the generalization of the learned representation. To address this issue, we propose a novel perspective of face anti-spoofing that focuses on the normalization selection in the feature extraction process. Concretely, an Adaptive Normalized Representation Learning (ANRL) framework is devised, which adaptively selects feature normalization methods according to the inputs, aiming to learn domain-agnostic and discriminative representation. Moreover, to facilitate the representation learning, Dual Calibration Constraints are designed, including Inter-Domain Compatible loss and Inter-Class Separable loss, which provide a better optimization direction for generalizable representation. Extensive experiments and visualizations are presented to demonstrate the effectiveness of our method against the SOTA competitors.
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
University of Electronic Science and Technology of China（电子科技大学）；National University of Singapore（新加坡国立大学）；National University of Defense Technology（国防科技大学）
Learning Representation and Synergy Invariances: A Povable Framework for Generalized Multimodal Face Anti-Spoofing
Beihang University（北京航空航天大学）；Nanyang Technological University（南洋理工大学）；Westlake University（西湖大学）；Great Bay University（大亚湾大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）
Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology（脑科学与智能技术卓越创新中心）；Chinese Academy of Sciences, Artificial Intelligence Research（中国科学院人工智能研究院）
FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning
State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江区）区块链与数据安全研究院）
Scale Propagation Network for Generalizable Depth Completion
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）；Intelligent Transportation Thrust of the Systems Hub, Hong Kong University of Science and Technology (GZ)（香港科技大学（广州）系统枢纽智能交通方向）；Multimodal Experiences Lab, Dolby Laboratories Inc（杜比实验室公司多模态体验实验室）
A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所麦康奈尔脑成像中心）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）
