Training a generalizable 3D part segmentation network is quite challenging but of great importance in real-world applications. To tackle this problem, some works design task-specific solutions by translating human understanding of the task to machine's learning process, which faces the risk of missing the optimal strategy since machines do not necessarily understand in the exact human way. Others try to use conventional task-agnostic approaches designed for domain generalization problems with no task prior knowledge considered. To solve the above issues, we propose AutoGPart, a generic method enabling training generalizable 3D part segmentation networks with the task prior considered. AutoGPart builds a supervision space with geometric prior knowledge encoded, and lets the machine to search for the optimal supervisions from the space for a specific segmentation task automatically. Extensive experiments on three generalizable 3D part segmentation tasks are conducted to demonstrate the effectiveness and versatility of AutoGPart. We demonstrate that the performance of segmentation networks using simple backbones can be significantly improved when trained with supervisions searched by our method.
Surrogate Supervision for Robust and Generalizable Deformable Image Registration
Department of Electrical and Computer Engineering, Vanderbilt University（维斯尼尔大学电气与计算机工程系）；Department of Radiology and Radiological Science, Johns Hopkins Medical School（约翰霍普金斯医学学校放射学与放射科学系）；Image Analysis and Communications Laboratory in the Department of Electrical and Computer Engineering, Johns Hopkins University（约翰霍普金斯大学电气与计算机工程系图像分析与通信实验室）；Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center（维斯尼尔大学医学中心认知医学中心）；University of Pittsburgh, School of Medicine（匹兹堡大学医学院）
Align, Don't Blend: Orthogonal Supervision Disentanglement As the Key to Generalizable AI-Generated Image Detection
Tencent Youtu Lab；East China University of Science；Shenzhen University；Hong Kong University of Science；Department of XXX, University of YYY, Location, Country；School of ZZZ, Institute of WWW, Location, Country
ProMMSearchAgent: A Generalizable Multimodal Search Agent Trained with Process-Oriented Rewards
East China Normal University（东中国师范大学）；Shanghai Innovation Institute（上海创新研究院）；Huawei Noah's Ark Lab（华为诺亚实验室）；Independent Researcher（独立研究者）；University College London（伦敦大学学院）
Scene-Generalizable Interactive Segmentation of Radiance Fields
Harbin Institute of Technology（哈尔滨工业大学）；Y-tech, Kuaishou Technology（快手科技Y-tech）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；Dartmouth College（达特茅斯学院）
Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Academy of Medical Engineering and Translational Medicine, Tianjin University（天津大学医学工程与转化医学研究院）；Hangzhou Institute of Medicine, Chinese Academy of Sciences（杭州医学研究所）；Yale University（耶鲁大学）
DuMeta++: Spatiotemporal Dual Meta-Learning for Generalizable Few-Shot Brain Tissue Segmentation Across Diverse Ages
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）
Unified Coding for Both Human Perception and Generalized Machine Analytics with CLIP Supervision
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））；Peng Cheng Laboratory（鹏城实验室）；Shenzhen University（深圳大学）；City University of Hong Kong（香港城市大学）
