Missing Modality Data

Missing modality data, a prevalent issue in multimodal learning, hinders accurate model training and inference across diverse applications like transportation mode detection and medical image analysis. Current research focuses on developing robust models that handle missing data during both training and testing phases, employing techniques such as masked autoencoders, multimodal transformers with attention mechanisms, and co-training strategies to reconstruct missing information or learn from incomplete data. These advancements are crucial for improving the reliability and generalizability of multimodal systems in real-world scenarios where complete data is often unavailable, impacting fields ranging from healthcare diagnostics to human activity recognition.