ummary: The paper presents Visual ChatGPT, a system that integrates different Visual Foundation Models to enable users to interact with ChatGPT by sending and receiving both languages and images, providing complex visual questions or editing instructions that require the collaboration of multiple AI models with multi-steps, and giving feedback and asking for corrected results.

Key insights and lessons learned:

• ChatGPT has remarkable conversational competency and reasoning capabilities, but it is currently not capable of processing or generating images from the visual world.
• Visual Foundation Models have great visual understanding and generation capabilities, but they are only experts on specific tasks with one-round fixed inputs and outputs.
• Visual ChatGPT combines the strengths of both language and visual models to enable complex interactions between users and AI models.
• The system design and prompt construction of Visual ChatGPT require careful consideration of the different inputs/outputs and visual feedback required by the integrated models.
• Visual ChatGPT opens up opportunities for investigating the visual roles of ChatGPT with the help of Visual Foundation Models.

Questions for the authors:

1. What were some of the biggest challenges you faced when designing and implementing Visual ChatGPT?
2. How do you envision Visual ChatGPT being used in practical applications, and what kinds of use cases do you see it being most effective in?
3. Can you describe any potential limitations or drawbacks of Visual ChatGPT that users and developers should be aware of?
4. Are there any particular Visual Foundation Models that you think work especially well with ChatGPT, or that you would recommend for developers looking to integrate visual models into their conversational AI systems?
5. What are some of the most promising avenues for future research on Visual ChatGPT, and how do you see this technology evolving over time?

Suggestions for future research:

1. Investigating the performance of Visual ChatGPT on different kinds of visual inputs and in different domains, such as healthcare or education.
2. Exploring the potential for incorporating other types of non-language inputs, such as audio or haptic feedback, into conversational AI systems.
3. Developing new techniques for prompt construction and user feedback that can improve the performance and usability of conversational AI systems like Visual ChatGPT.
4. Examining the ethical implications of integrating visual models into conversational AI, such as issues related to bias, privacy, and accountability.
5. Comparing the performance of Visual ChatGPT to other state-of-the-art conversational AI systems, and identifying areas where it excels or falls short.

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