Multimodal Maestro: Advanced LMM Prompting

Multimodality, which combines data inputs like text, video, and audio, is poised to become a significant focus for AI development, yet current Large Multimodal Models (LMMs) often struggle with tasks beyond Object Character Recognition and Visual Question Answering. To address these limitations, the Multimodal Maestro library offers advanced prompting strategies that enhance LMM capabilities, enabling functions such as object detection and segmentation by incorporating marks generated by models like GroundingDINO and Segment Anything Model. This technique, termed Set-of-Mark Prompting, has demonstrated improved performance in models like GPT-4 Vision, allowing for more accurate visual grounding. Additionally, CogVLM, another LMM, showed remarkable results using the same method, highlighting its potential as a competitor to GPT-4 Vision. Despite these advancements, the blog identifies a need for more user-friendly interfaces to interact with LMMs and announces ongoing efforts to expand the Multimodal Maestro library with new strategies to optimize LMM performance.