Modality Specific Information

Modality-specific information refers to the unique details contained within individual data types (modalities) like images, text, or audio, which are often combined in multimodal learning tasks. Current research focuses on developing methods to effectively leverage both shared and unique information across modalities, often employing techniques like contrastive learning, disentangled representations, and dynamic fusion networks within transformer or other deep learning architectures. This work aims to improve the robustness and accuracy of multimodal systems by addressing challenges such as missing data and modality discrepancies, with significant implications for applications ranging from medical diagnosis and person re-identification to language understanding and action quality assessment.