College of Software, Jilin University（吉林大学软件学院）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education（教育部符号计算与知识工程重点实验室）；School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）；Engineering Research Center of Intelligent Finance, Ministry of Education（教育部智能金融工程研究中心）；Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology（哈尔滨工业大学深圳视觉对象检测与识别重点实验室）；School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）
Cheng Peng, Yonghao Li, Wanfu Gao, Jie Wen, Weiping Ding
备注：
Accepted to IEEE Transactions on Image Processing
发表信息：
IEEE Transactions on Image Processing, vol. 35, pp. 10972-10986, 2026
In recent years, multi-view multi-label learning (MVML) has attracted extensive attention due to its close alignment to real-world scenarios. Information-theoretic methods have gained prominence for learning nonlinear correlations. However, two key challenges persist: first, features in real-world data commonly exhibit high-order structural correlations, but existing information-theoretic methods struggle to learn such correlations; second, commonly relying on heuristic optimization, information-theoretic methods are prone to converging to local optima. To address these two challenges, we propose a novel method called Structural Entropy Guided High-Order Correlation Learning for Multi-View Multi-Label Feature Selection (SEHFS). The core idea of SEHFS is to convert the feature graph into a structural-entropy-minimizing encoding tree, quantifying the information cost of high-order dependencies and thus learning high-order feature correlations beyond pairwise correlations. Specifically, features exhibiting strong high-order redundancy are grouped into a single cluster within the encoding tree, while inter-cluster feaeture correlations are minimized, thereby eliminating redundancy both within and across clusters. Furthermore, a new framework based on the fusion of information theory and matrix methods is adopted, which learns a shared semantic matrix and view-specific contribution matrices to reconstruct a global view matrix, thereby enhancing the information-theoretic method and balancing the global and local optimization. The ability of structural entropy to learn high-order correlations is theoretically established, and and both experiments on eight datasets from various domains and ablation studies demonstrate that SEHFS achieves superior performance in feature selection.
D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement
Key Laboratory of DECV, Chongqing University of Posts and Telecommunications（重庆邮电大学信息与通信工程重点实验室）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；School of Informatics, Aristotle University of Thessaloniki（希腊塞萨洛尼基阿里斯托芬大学信息学院）
Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment
Department of Mathematics and Statistics, Université de Montréal, Montreal, Canada（蒙特利尔大学数学与统计学系）；Mila - Quebec AI Institute, Montreal, Canada（魁北克人工智能研究所）；Department of Mathematics and Statistics, Utah State University, Logan, Utah, USA（犹他州立大学数学与统计学系）；Department of Statistics, Brigham Young University, Provo, Utah, USA（Brigham Young 大学统计学系）
Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments
SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Guangzhou Research Translation and Innovation Institute, National University of Singapore（新加坡国立大学广州研究翻译与创新研究所）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑网络与脑机接口重点实验室）；Brainnetome Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑网络中心）
College of Software Engineering, Southeast University（东南大学软件学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Yam Pak Charitable Foundation School of Computing and Information Sciences, Saint Francis University（圣弗朗西斯大学叶荫堂计算与信息科学学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Pengcheng Laboratory, Shenzhen, China（鹏城实验室）
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；University of Central Florida（中佛罗里达大学）；University of Macau（澳门大学）
Semantic-guided Representation Learning for Multi-Label Recognition
Chongqing University of Posts and Telecommuncation, China（重庆邮电大学）；Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China（重庆绿色智能技术研究院，中国科学院）；Singapore Management University, Singapore（新加坡国立大学）
Rep-GLS: Report-Guided Generalized Label Smoothing for Robust Disease Detection
Arizona State University（亚利桑那州立大学）；Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences（广州生物医学研究院，中国科学院）；Kunming Medical University（昆明医科大学）；Northumbria University（北umbria大学）；NII, Tokyo Institute of Technology（东京技术大学国家研究所）；RIKEN（理化学研究所）；Westlake University（西湖大学）
Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification
1 Department of Statistics, Brigham Young University, Provo, UT, USA Emails；2 Department of Computer Science, Brigham Young University, Provo, UT, USA Email；3 Department of Public Health, Brigham Young University, Provo, UT, USA Email；4 Department of Neurology, Vagelos College of Physicians \& Surgeons, Columbia University, New York, NY, USA Email；5 Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA Email；6 Elmhurst Hospital Center, NYC Health + Hospitals, New York, NY, USA Email；7 Department of Mathematics \& Statistics, Utah State University, Logan, UT, USA Email
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
Southern University of Science and Technology（南方科技大学）；Hong Kong Baptist University（香港浸会大学）；University of Melbourne（墨尔本大学）；University of Technology Sydney（悉尼科技大学）；Huazhong Agricultural University（华中农业大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Sydney（悉尼大学）
Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding
Shanghai Acoustics Laboratory, Chinese Academy of Sciences（中国科学院上海声学实验室）；University of Chinese Academy of Sciences（中国科学院大学）；The Shenzhen Research Institute, The Hong Kong Polytechnic University（香港理工大学深圳研究院）；The Hong Kong Polytechnic University（香港理工大学）
Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network
University of Science and Technology of China（中国科学技术大学）；University of Waterloo（滑铁卢大学）；CISPA Helmholtz Center for Information Security（CISPA赫尔姆霍茨信息安全中心）
Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation
college of physics and information engineering, University of Fuzhou University（福州大学物理与信息工程学院）；Faculty of Applied Science, University of Macao Polytechnic University（澳门理工大学应用科学学院）；University of Toronto University（多伦多大学）
