Visual question answering with grounding and user selection priority

Introduction

Enables users to query images using text input. Users can select specific objects for their queries, streamlining the process of asking questions and eliminating the need to describe the position of objects within the image using spatial words.

Architecture

🔗 System requirements

• Requires a CUDA compatible GPU with minimum 8gb VRAM
• Python>=3.8

🚀 Quick Start

• Add your GPT-4 Vision API Key using a .env file defined as OPENAI_API_KEY

from dotenv import load_dotenv
load_dotenv()

• Clone the repository

git clone https://github.com/shetumohanto/visual-question-answering.git
cd visual-question-answering

• Download checkpoint for the segment anything vit_h model.

wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

• Install required dependencies

pip install -r requirements.txt

• Run Intelligent EYE

streamlit run app.py

🔗 Related Works

This project uses the following technologies at its core architecture:

• Segment Anything: Segment anything
• SoM: Set-of-Mark Visual Prompting for GPT-4V

🔗 Sample output