Improving the understanding of images and text using AI (part 2)

This article is the second part of the series where the author directs his efforts to create an advanced version of preliminary use, which performs analytics of discussions on images or videos, like a helper. This means you can ask and learn more about your input content.

🚀 In part 1 of this short biparter series, we have developed an application that converts images to audio descriptions using vision-language and text-speech models. We combined the image-to-text that analyzes and understands the image by generating a description with a text-to-language model to create an audio description that helps people with vision problems.

💡 Instead, we take a step forward. Instead of simply providing audio descriptions, we create an interactive conversation about images or videos. This is known as Conversational AI - technology that allows users to talk to chat systems, virtual assistants or agents.

⚡ We use LLAVA, a model that combines understanding of images and spoken opportunities. After creating our tools, we explore multi -modal models that can handle images, videos, text, audio and more, all at once to give you even more options and ease for your applications.

• 📌 Definition and explanation of Visual Instruction Tuning technology.
• 📌 LLAVA integration processes into our application.
• 📌 Using Whisper for text-to-speech.
• 📌 Consideration of other multimodal models that can process images, text, audio and more.