Multi Modal Model

Multi-modal models aim to integrate and process information from multiple data sources (e.g., text, images, audio) to achieve a more comprehensive understanding than unimodal approaches. Current research focuses on improving model robustness, efficiency, and generalization across diverse tasks, often employing transformer-based architectures and techniques like self-supervised learning, fine-tuning, and modality fusion strategies. These advancements are significant for various applications, including assistive robotics, medical image analysis, and improved large language model capabilities, by enabling more accurate and nuanced interpretations of complex real-world data.