Multimodal Model

Multimodal models integrate information from multiple sources like text, images, audio, and video to achieve a more comprehensive understanding than unimodal approaches. Current research focuses on improving model interpretability, addressing biases, enhancing robustness against adversarial attacks and missing data, and developing efficient architectures like transformers and state-space models for various tasks including image captioning, question answering, and sentiment analysis. These advancements are significant for applications ranging from healthcare and robotics to more general-purpose AI systems, driving progress in both fundamental understanding and practical deployment of AI.