Multimodal GPT

Multimodal GPT models aim to extend the capabilities of large language models (LLMs) by integrating visual and other sensory information, enabling them to understand and respond to complex instructions involving both text and images. Current research focuses on developing efficient training methods, such as instruction tuning and low-rank adaptation, to enhance model performance and reduce computational costs, often leveraging pre-trained models like OpenFlamingo and adapting them for multimodal tasks. These advancements are significant because they enable more robust and versatile AI systems with applications ranging from autonomous driving assistance to personalized image generation and improved human-computer interaction.