解决多模态数据缺失问题，提升模型鲁棒性
Deep Multimodal Learning with Missing Modality: A Survey
- 提出处理多模态数据缺失的深度学习方法
- 系统梳理现有技术、数据集与应用方向
- 适合研究多模态融合与实际部署的学者
在多模态模型训练与测试中，由于传感器限制、成本约束、隐私顾虑或数据丢失，某些模态数据可能缺失，严重影响性能。针对多模态缺失问题的学习方法可通过增强模型鲁棒性，使其在部分模态不可用时仍保持稳定表现。本文首次全面综述了深度学习背景下的多模态学习中缺失模态（MLMM）研究进展，明确阐述了其与标准多模态学习的区别与动机，系统分析了当前主流方法、应用场景及常用数据集，并总结了现存挑战与未来方向。
原文摘要 · Abstract (English)
During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。