A Comprehensive Survey on Deep Learning Methods for Multimodal Learning with Missing Modalities

Multimodal learning systems often face the challenge of missing or incomplete data in real-world applications. This survey provides a comprehensive overview of recent deep learning techniques that address the problem of Multimodal Learning with Missing Modality (MLMM), including modality augmentation, feature space engineering, architecture engineering, and model selection approaches.