Gemini Pro Vision is an innovative AI-powered Visual Question Answering (VQA) solution. This cutting-edge project enables users to ask questions about images and receive accurate, natural language responses, revolutionizing the way we interact with and extract insights from visual data.

At the core of Gemini Pro Vision lies a sophisticated combination of computer vision and natural language processing techniques. By leveraging state-of-the-art deep learning models for object detection, scene understanding, and text generation, the system can comprehend the content of an image and generate human-like responses to user queries.

The modular and scalable architecture allows for seamless integration of new knowledge domains and question types, ensuring the system's adaptability to a wide range of applications.

Visual Question Answering (VQA) where the model interprets images and text together to respond to inquiries about an image. This function demonstrates the potent combination in multimodal AI, enabling models like Gemini Pro Vision to not only perceive but also analyze and articulate visual data within a meaningful context, greatly enhancing user engagement and accessibility.

Image Analysis: The model uses computer vision techniques to understand the content and context of the image. This might involve recognizing objects, their attributes, and relationships between them.

Question Understanding: The model processes the question, often using natural language processing techniques, to understand what is being asked.

Answer Generation: After analyzing both the image and the question, the model synthesizes the information to produce an answer. This can be in various forms, such as a word, phrase, sentence, or even a number.

Input: text and images

Output: text

Can take multimodal inputs, text and image.

Can handle zero, one, and few-shot tasks.

Technology Stack:

Google Gemini: For providing the underlying language model.

Streamlit: For the user interface framework.

Python

Hugging Face Transformers

This application is an image annotation app that uses the Gemini AI model to generate annotations for images. The app allows users to upload images and input a natural language description of the objects or features they want to get description from the image. The model then generates the corresponding annotations and overlays them on the image.

This interactive demo leverages generative adversarial networks (GANs) to allow users to create custom images with textual descriptions or sketches. I built the backend AI model using StyleGAN - a state-of-the-art GAN framework that can generate high-resolution, photorealistic images from text and crude image inputs.

The easy-to-use Streamlit interface accepts user inputs like "a red flower in a blue vase on a wooden table" and my customized AI model churns the text prompt through NLP processing to convert language into a specified digital image reflecting the user instructions. It handles abstract ideas to real objects seamlessly

The easy-to-use Streamlit interface accepts user inputs like "a red flower in a blue vase on a wooden table" and my customized AI model churns the text prompt through NLP processing to convert language into a specified digital image reflecting the user instructions. It handles abstract ideas to real objects seamlessly.

Users can also opt to provide a simple sketch to guide the model using the left pane. The application feeds the doodle along with text into the StyleGAN generator to output custom images tailored to each unique combination. An auto-refresh option continually iterates on the image as the model improvises and user re-adjusts guidance.

Python - Programming language

Streamlit - Web framework

Gemini AI Model - AI model for generating image annotations