Multimodal fusion is crucial in joint decision-making systems for rendering holistic judgments. Since multimodal data changes in open environments, dynamic fusion has emerged and achieved remarkable progress in numerous applications. However, most existing dynamic multimodal fusion methods lack theoretical guarantees and easily fall into suboptimal problems, yielding unreliability and instability. To address this issue, we propose a Predictive Dynamic Fusion (PDF) framework for multimodal learning. We proceed to reveal the multimodal fusion from a generalization perspective and theoretically derive the predictable Collaborative Belief (Co-Belief) with Mono- and Holo-Confidence, which provably reduces the upper bound of generalization error. Accordingly, we further propose a relative calibration strategy to calibrate the predicted Co-Belief for potential uncertainty. Extensive experiments on multiple benchmarks confirm our superiority. Our code is available at https://github.com/Yinan-Xia/PDF.
Meta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning
Department of Statistics, University of California, Irvine, CA, USA（统计学系，加州大学伊维奇分校）；Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA（统计学与应用概率系，加州大学圣巴巴拉分校）
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation
Valeo Schalter und Sensoren GmbH（法雷奥开关与传感器有限公司）；Stellantis, Opel Automobile GmbH（斯泰兰蒂斯集团欧宝汽车有限公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Ilmenau University of Technology（伊尔梅瑙工业大学）
JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation
Jiangsu University（江苏大学）；University of Southern California（南加州大学）；Peking University（北京大学）；Nanyang Technological University（南洋理工大学）；Brown University（布朗大学）；UC San Diego（加州大学圣地亚哥分校）；Stony Brook University（石溪大学）；University of the Chinese Academy of Sciences（中国科学院大学）
EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception
Hunan University（湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；Nanjing University of Posts and Telecommunications（南京邮电大学）
PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation
Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；University of Adelaide（阿德莱德大学）；Wuhan University（武汉大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Jiaotong University（北京交通大学）；AIR Wuxi Innovation Center, Tsinghua University（清华大学无锡创新中心）；Lenovo（联想集团）
CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model
Northwestern Polytechnical University（西北工业大学）；Research Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Shandong Normal University（山东师范大学）
Multimodal Attention-Aware Fusion for Diagnosing Distal Myopathy: Evaluating Model Interpretability and Clinician Trust
Information Systems Group, Eindhoven University of Technology, The Netherlands（埃因霍温技术大学信息系统组）；Eindhoven Artificial Intelligence Systems Institute, The Netherlands（埃因霍温人工智能系统研究所）；Department of Computer Science and Technology, University of Cambridge, United Kingdom（剑桥大学计算机科学与技术系）；Department of Medicine - DIMED, Padua University Hospital, Italy（帕多瓦大学医院医学部-DIMED）；Department of Environmental Sciences, Informatics, and Statistics, Ca’ Foscari University of Venice, Italy（威尼斯卡弗里大学环境科学、信息学与统计学系）
Dynamic Disentangled Fusion Network for RGBT Tracking
Anhui University（安徽大学）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
Dynamic Feature Fusion for Semantic Edge Detection
Institute of Remote Sensing and Digital Earth, CAS（中国科学院遥感与数字地球研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）
