Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
University of Helsinki（赫尔辛基大学）；University of Technology Sydney（悉尼科技大学）；Beihang University（北京航空航天大学）；Monash University（莫纳什大学）；Aalto University（阿尔托大学）；Griffith University（格里菲斯大学）；Amazon（亚马逊公司）；Columbia University（哥伦比亚大学）
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（工信部模式分析与机器智能重点实验室）
Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications
Business and IoT Integrated Solutions LTD（业务与物联网集成解决方案有限公司）；Trinity College Dublin（三一学院都柏林）；University of Thessaly（塞萨洛尼基大学）；University of Western Macedonia（西方马其顿大学）
A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends
Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；The University of Sydney（悉尼大学）；Nanyang Technological University（南洋理工大学）；JD Explore Academy（京东探索研究院）；Chinese Academy of Sciences（中国科学院）；Alibaba Group（阿里巴巴集团）
Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery（大数据研究所，李嘉树健康信息与发现中心）；University of Oxford（牛津大学）；Nuffield Department of Population Health（人口健康部门）；Nuffield Department of Clinical Neurosciences（临床神经科学部门）；Department of Engineering Science（工程科学系）；School of Engineering, College of Engineering and Physical Sciences（工程学院，工程与物理科学学院）；University of Birmingham（伯明翰大学）
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions
University of Georgia（佐治亚大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Harvard University（哈佛大学）；Carnegie Mellon University（卡内基梅隆大学）；Vanderbilt University（范德堡大学）；Mayo Clinic Arizona（亚利桑那梅奥诊所）；Augusta University（奥古斯塔大学）
Neural Spatiotemporal Point Processes: Trends and Challenges
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Boston College（波士顿学院）；University of Kaiserslautern–Landau（凯泽斯劳滕-兰道大学）；Mohammed VI Polytechnic University（穆罕默德六世理工大学）；Georgia Institute of Technology（佐治亚理工学院）
