Individual Modality Model

Individual modality models focus on leveraging the unique information provided by single data sources (e.g., images, text, audio) for tasks like object recognition, action classification, or embedding generation. Current research emphasizes developing robust models that handle missing data, modality heterogeneity, and imbalanced datasets, often employing techniques like meta-learning, mixture-of-experts architectures, and specialized loss functions (e.g., focal loss variants). These advancements are improving performance in diverse applications, including medical image analysis, e-commerce, and surgical procedure automation, by enabling more accurate and efficient processing of complex multimodal data.