Single domain generalization is a challenging case of model generalization, where the models are trained on a single domain and tested on other unseen domains. A promising solution is to learn cross-domain invariant representations by expanding the coverage of the training domain. These methods have limited generalization performance gains in practical applications due to the lack of appropriate safety and effectiveness constraints. In this paper, we propose a novel learning framework called progressive domain expansion network (PDEN) for single domain generalization. The domain expansion subnetwork and representation learning subnetwork in PDEN mutually benefit from each other by joint learning. For the domain expansion subnetwork, multiple domains are progressively generated in order to simulate various photometric and geometric transforms in unseen domains. A series of strategies are introduced to guarantee the safety and effectiveness of the expanded domains. For the domain invariant representation learning subnetwork, contrastive learning is introduced to learn the domain invariant representation in which each class is well clustered so that a better decision boundary can be learned to improve it's generalization. Extensive experiments on classification and segmentation have shown that PDEN can achieve up to 15.28% improvement compared with the state-of-the-art single-domain generalization methods.
Shape Robust Text Detection with Progressive Scale Expansion Network
Nanjing University（南京大学）；Tongji University（同济大学）；Nanjing University of Science and Technology（南京理工大学）；Megvii (Face++) Technology Inc.（旷视（Face++）科技有限公司）；Momenta
Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery
Computer Vision Center, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；Fairfield University（费尔菲尔德大学）；Michigan Technological University（密歇根理工大学）；University of Kerala（喀拉拉大学）；ESPOL Polytechnic University（ESPOL理工大学）；University of Mississippi（密西西比大学）
LGCA: Enhancing Semantic Representation via Progressive Expansion
University of Science, Vietnam National University Ho Chi Minh City, Vietnam（越南胡志明市国家大学科学大学）；National University of Singapore, Singapore（新加坡国立大学）；AISIA Lab, Ho Chi Minh City, Vietnam（越南胡志明市AISIA实验室）
MGPC: Multimodal Network for Generalizable Point Cloud Completion With Modality Dropout and Progressive Decoding
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所 multimodal 人工智能系统国家重点实验室）；Chemical Defense Institute, Academy of Military Sciences（军事科学院化学防御研究院）；Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所 复杂系统认知与决策智能重点实验室）
Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation
School of Computer Science and Cyber Engineering, Guangzhou University（计算机科学与网络安全学院，广州大学）；Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University（智能信息处理重点实验室，电子与信息工程学院，深圳大学）；School of Electronics and Communication Engineering, Guangzhou University（电子与通信工程学院，广州大学）；Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164（里昂大学，INSA里昂，CNRS，IETR - UMR 6164）；Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Institute of Artificial Intelligence and Advanced Communication, College of Electronics and Information Engineering, Shenzhen University（智能信息处理重点实验室，先进机器学习与应用重点实验室，人工智能与高级通信研究所，电子与信息工程学院，深圳大学）；College of Mathematics and Statistics, Shenzhen University（数学与统计学院，深圳大学）
Progressive Dual Priori Network for Generalized Breast Tumor Segmentation
Guizhou University（贵州大学）；Harbin Medical University Cancer Hospital（哈尔滨医科大学附属肿瘤医院）；Univ Lyon（里昂大学）；INSA Lyon（里昂国立应用科学学院）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CREATIS（CREATIS研究所）
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks
State Key Laboratory of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；The University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）
Progressive Feedback-Enhanced Transformer for Image Forgery Localization
State Key Laboratory of Media Convergence and Communication（媒体融合与传播国家重点实验室）；Communication University of China（中国传媒大学）；School of Computer and Cyber Sciences（计算机与网络科学学院）
