Modality Heterogeneity

Modality heterogeneity, the challenge of integrating data from diverse sources (e.g., text, images, sensor readings), is a central problem in multimodal machine learning. Current research focuses on developing robust fusion methods, often employing transformer architectures or contrastive learning, to effectively combine heterogeneous data and address issues like missing modalities and asynchronous data streams. These advancements are crucial for improving performance in various applications, including emotion recognition, medical diagnosis, and autonomous driving, where integrating information from multiple sensors is essential for accurate and reliable outcomes. The ultimate goal is to create systems that can seamlessly leverage the strengths of different data modalities to achieve superior performance compared to unimodal approaches.