Recently, prompt learning has garnered considerable attention for its success in various Vision-Language (VL) tasks. However, existing prompt-based models are primarily focused on studying prompt generation and prompt strategies with complete modality settings, which does not accurately reflect real-world scenarios where partial modality information may be missing. In this paper, we present the first comprehensive investigation into prompt learning behavior when modalities are incomplete, revealing the high sensitivity of prompt-based models to missing modalities. To this end, we propose a novel Multi-step Adaptive Prompt Learning (MuAP) framework, aiming to generate multimodal prompts and perform multi-step prompt tuning, which adaptively learns knowledge by iteratively aligning modalities. Specifically, we generate multimodal prompts for each modality and devise prompt strategies to integrate them into the Transformer model. Subsequently, we sequentially perform prompt tuning from single-stage and alignment-stage, allowing each modality-prompt to be autonomously and adaptively learned, thereby mitigating the imbalance issue caused by only textual prompts that are learnable in previous works. Extensive experiments demonstrate the effectiveness of our MuAP and this model achieves significant improvements compared to the state-of-the-art on all benchmark datasets
Enhancing Foundation VLM Robustness to Missing Modality: Scalable Diffusion for Bi-directional Feature Restoration
Department of Electronics；Information \ 'an Jiaotong University Xi'an China；School of Information；Communications Engineering \ 'an Jiaotong University Xi'an China；School of Mathematics；Statistics \ 'an Jiaotong University Xi'an China；Information \ 'an Jiaotong University；Communications Engineering \ 'an Jiaotong University；Statistics \ 'an Jiaotong University
ADMC: Attention-based Diffusion Model for Missing Modalities Feature Completion
National University of Defense Technology（国防科学技术大学）；The Fifth Electronic Research Institute of MIIT（信息产业部第五电子研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology Beijing（北京科技大学）；Hunan University（湖南大学）
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；Singapore University of Social Sciences, Singapore（新加坡社会科学研究大学）
Deep Multimodal Learning with Missing Modality: A Survey
The Australian National University（澳大利亚国立大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Adelaide University（阿德莱德大学）；The University of Surrey（萨里大学）
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
NepAl Applied Mathematics and Informatics Institute（尼泊尔应用数学与信息学研究所）；Fogsphere (Redev.AI Ltd)（福格球（Redev.AI有限公司））；University of Lausanne（洛桑大学）；West Virginia University（西弗吉尼亚大学）；University of Verona（维罗纳大学）；University College London（伦敦大学学院）；University of Aberdeen（阿伯丁大学）
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment
School of Robotics Xi’an Jiaotong-Liverpool University Suzhou, China；School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou, China；School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou, China；Data Science Research Center Duke Kunshan University Suzhou, China
Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection
Department of Computer Engineering, Ankara University（安卡拉大学计算机工程系）；Department of Modeling and Simulation, Graduate School of Informatics, METU（信息学院建模与模拟系，METU）
CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（上海智能自主系统研究院，同济大学）；College of Biomedical Engineering, Fudan University（生物医学工程学院，复旦大学）；School of Medicine, Tongji University（医学院，同济大学）；College of Information Engineering, China Jiliang University（信息工程学院，中国嘉兴大学）
Co-Learning for Missing Arbitrary Modalities in Multi-modal Classification
GFZ Helmholtz Center for Geosciences（德国波茨坦地学研究中心亥姆霍兹中心）；INRAE, UMR TETIS, University of Montpellier（法国蒙彼利埃大学农业环境与生态研究院、蒙彼利埃大学TETIS联合研究单位）；INRIA, EVERGREEN, University of Montpellier（法国蒙彼利埃大学信息与自动化研究所、EVERGREEN实验室）；CIRAD, UMR TETIS, University of Montpellier（法国蒙彼利埃大学国际农业研究磋商组织、蒙彼利埃大学TETIS联合研究单位）
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
Zhejiang University（浙江大学）；The University of Manchester（曼彻斯特大学）；University of Oxford（牛津大学）；The University of Edinburgh（爱丁堡大学）；Tsinghua University（清华大学）；Donghai laboratory（东海实验室）
PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
Department of Computer Science, University of Central Florida（中央佛罗里达大学计算机科学系）；Department of Computer Science, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校计算机科学系）
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
Hefei University of Technology（合肥工业大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Academy of Cyber（网络空间研究院）；Institute of Artificial Intelligence（人工智能研究院）
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios
Institute of Computational Perception, Johannes Kepler University Linz（约翰开普勒林茨大学计算感知研究所）；Human-centered AI Group, AI Lab, Linz Institute of Technology（林茨理工大学人工智能实验室以人为中心的人工智能组）
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；Guangxi Medical University（广西医科大学）；The Hong Kong University of Science and Technology（香港科技大学）
