The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学健康医疗大数据国家研究院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Data and Computer Science, Shandong Women’s University（山东女子学院数据与计算机科学学院）
Muyang Qiu, Jian Zhang, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao
Despite the recent success of domain generalization in medical image segmentation, voxel-wise annotation for all source domains remains a huge burden. Semi-supervised domain generalization has been proposed very recently to combat this challenge by leveraging limited labeled data along with abundant unlabeled data collected from multiple medical institutions, depending on precisely harnessing unlabeled data while improving generalization simultaneously. In this work, we observe that domain shifts between medical institutions cause disparate feature statistics, which significantly deteriorates pseudo-label quality due to an unexpected normalization process. Nevertheless, this phenomenon could be exploited to facilitate unseen domain generalization. Therefore, we propose 1) multiple statistics-individual branches to mitigate the impact of domain shifts for reliable pseudo-labels and 2) one statistics-aggregated branch for domain-invariant feature learning. Furthermore, to simulate unseen domains with statistics difference, we approach this from two aspects, i.e., a perturbation with histogram matching at image level and a random batch normalization selection strategy at feature level, producing diverse statistics to expand the training distribution. Evaluation results on three medical image datasets demonstrate the effectiveness of our method compared with recent SOTA methods. The code is available at https://github.com/qiumuyang/SIAB.
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）
MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation
School of Computer Science, Wuhan University（武汉大学计算机学院）；University of Louisville（路易斯维尔大学）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）
ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
School of Computer Science and Engineering, Northeastern University, Shenyang, China（计算机科学与工程学院，东北大学，沈阳，中国）；Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China（教育部医学图像智能计算重点实验室，东北大学，沈阳，中国）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China（工业智能与系统优化国家级前沿科学中心，沈阳，中国）；Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada（阿尔伯塔人工智能研究所，阿尔伯塔大学，埃德蒙顿，加拿大）
Beyond Semantic Priors: Mitigating Optimization Collapse for Generalizable Visual Forensics
Institute of Information Engineering, Chinese Academy of Sciences（信息工程研究所，中国科学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学信息与科学学院）
CoIn3D: Revisiting Configuration-Invariant Multi-Camera 3D Object Detection
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, 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人机混合增强智能全国重点实验室）；Intelligent Transportation Thrust of the Systems Hub, HKUST(GZ)（香港科技大学（广州）系统枢纽智能交通学域）；Amazon Alexa AI（亚马逊Alexa人工智能）
Diffusion-Guided Mask-Consistent Paired Mixing for Endoscopic Image Segmentation
School of Intelligent Engineering, Sun Yat-sen University (Shenzhen Campus)（中山大学智能工程学院）；Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院耳鼻喉科）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Nanjing University of Information Science and Technology（南京信息工程大学）
Generalizable Knowledge Distillation from Vision Foundation Models for Semantic Segmentation
School of Artificial Intelligence, Xidian University, China（西安电子科技大学人工智能学院）；Department of Information Engineering and Computer Science, University of Trento, Italy（特伦托大学信息工程与计算机科学系）；Department of Automation, Tsinghua University, China（清华大学自动化系）；School of Computer Science and Information Engineering, Hefei University of Technology, China（合肥工业大学计算机科学与信息工程学院）
NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection
Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology（数字取证工程研究中心，教育部，南京信息科技大学）；University of Macau（澳门大学）
FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection
Shenzhen University（深圳大学）；Shandong Artificial Intelligence Institute（山东省人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Tianjin University of Technology（天津理工大学）
XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Institute of Big Data, Fudan University（复旦大学大数据研究院）；AI Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能方向）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou, 215123, China；Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China；Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou, 215123, China；State Key Laboratory of Precision \& Intelligent Chemistry, USTC, Hefei, China
Generalizable Image Repair for Robust Visual Control
University of Florida AI Scholars Program（佛罗里达大学人工智能学者计划）；Trustworthy Engineered Autonomy (TEA) Lab（可信工程自主性实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）AI thrust）；Sun Yat-sen University（中山大学）；The Hong Kong University of Science and Technology（香港科技大学）
GLOMIA-Pro: A Generalizable Longitudinal Medical Image Analysis Framework for Disease Progression Prediction
School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；Department of Thoracic Surgery, Sun Yat-sen University Cancer Center（中山大学肿瘤医院胸外科部门）；Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University（中山大学第五附属医院放射科部门）；Beijing Duanliu Medical Technology Co., Ltd.（北京杜流医疗科技有限公司）
CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）
Biometric data, such as face images, are often associated with sensitive information (e.g medical, financial, personal government records). Hence, a data breach in a system storing such information can have devastating consequences. Deep learning is widely utilized for face recognition (FR); however, such models are vulnerable to backdoor attacks executed by malicious parties. Backdoor attacks cause a model to misclassify a particular class as a target class during recognition. This vulnerability can allow adversaries to gain access to highly sensitive data protected by biometric authenticatio…
FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learning
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；Engineering Research Center of Intelligent Finance, Ministry of Education, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（智能金融工程研究中心，教育部，计算机与人工智能学院，西南财经大学）；School of Electronic and Information Engineering, Ningbo University of Technology（电子信息工程学院，宁波工业大学）
What Makes You Unique? Attribute Prompt Composition for Object Re-Identification
School of Information and Communication Engineering, Dalian University of Technology（信息与通信工程学院，大连理工大学）；School of Future Technology, School of Artificial Intelligence, Dalian University of Technology（未来技术学院、人工智能学院，大连理工大学）；Technical Architecture Department, WeChat AI, Tencent, China（技术架构部，微信AI，腾讯，中国）
Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery（先进工程机械智能制造国家重点实验室）
Online Prototype Learning for Online Continual Learning
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；MOE Frontiers Center for Brain Science, Fudan University（复旦大学教育部脑科学前沿中心）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
Rewiring Development in Brain Segmentation: Leveraging Adult Brain Priors for Enhancing Infant MRI Segmentation
Department of Information Engineering University of Brescia (Italy)（信息工程系，布雷西亚大学）；School of Psychology & Neuroscience University of Glasgow (UK)（心理学与神经科学学院，格拉斯哥大学）；Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health University of Brescia (Italy)（医学与外科专科、放射科学与公共卫生系，布雷西亚大学）
