Modality Agnostic

Modality-agnostic research aims to create machine learning models capable of processing and integrating information from diverse data types (e.g., text, images, audio) without requiring task-specific or modality-specific architectures. Current efforts focus on developing techniques like knowledge distillation from multi-modal models, learning modality-agnostic representations through mutual information minimization or contrastive learning, and employing unified sequence-to-sequence frameworks. This work is significant because it promises more robust and adaptable AI systems capable of handling incomplete or varied data, leading to improved performance in applications like medical image analysis, multimodal video understanding, and general-purpose AI.