Modality Independent

Modality-independent learning aims to create models capable of processing and integrating information from diverse data types (e.g., images, audio, text) without requiring modality-specific architectures. Current research focuses on developing algorithms that learn shared representations across modalities, often employing techniques like graph neural networks, optimal transport, and contrastive learning to handle data misalignments and limited labeled data. This approach promises to improve the efficiency and robustness of machine learning systems by enabling seamless integration of multimodal data, with applications ranging from medical image analysis to advanced audio-visual event classification and semantic-based information retrieval.