In ‘Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want,’ researchers introduced a new end-to-end trained Multimodal Large Language Model (MLLM) called SPHINX-V.

• SPHINX-V can understand various visual prompts and textual instructions.
• MDVP-Data is a multi-domain dataset containing over 1.6M image-visual prompt-text samples.
• MDVP-Bench provides a benchmark for visual prompting instruction comprehension.
• SPHINX-V shows remarkable improvements in pixel-level descriptions and Q&A abilities through visual prompting.

This research presents a significant leap in human-AI interaction, expanding the capacities of MLLMs to understand and respond to visual cues. Such advancements could be groundbreaking for various applications, including education, design, and accessible technology interfaces. Explore the project.