The article Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want introduces SPHINX-V, a Multimodal Large Language Model capable of processing multiple forms of visual prompts for enhanced interaction. Highlights of the paper include:

• SPHINX-V integrates a vision encoder, a visual prompt encoder, and an LLM for impressive multimodal interaction.
• The novel MDVP-Data dataset features 1.6M unique samples across various domains for cutting-edge visual prompting research.
• MDVP-Bench serves as a comprehensive benchmark for assessing visual prompting understanding in models.

This is a significant step forward for AI as it shows the ability of models to comprehend more complex, multimodal prompts, potentially leading to more intuitive human-AI interactions. This research lays the groundwork for future functionalities where MLLMs might interact with a wider range of inputs and contexts.