Deep Multimodal Learning with Missing Modality: A Survey

Renjie Wu, Hu Wang, Hsiang-Ting Chen, Gustavo Carneiro

Abstract: During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, which can degrade performance. Multimodal learning techniques that explicitly account for missing modalities aim to improve robustness by enabling models to perform reliably even when certain inputs are unavailable. This survey presents the first comprehensive review of Multimodal Learning with Missing Modality (MLMM), with a focus on deep learning approaches. We outline the motivations and key distinctions between MLMM and conventional multimodal learning, provide a detailed analysis of existing methods, applications, and datasets, and conclude by highlighting open challenges and future research directions.

Certifications: Survey Certification

Submission Length: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Stanislaw_Kamil_Jastrzebski1

Submission Number: 5971