Minigpt-4

🚀 Meet MiniGPT-4! 🤖✨ This advanced AI tool seamlessly combines language models with visual encoders to generate detailed image descriptions, craft websites, write stories/poems inspired by images, and even teach cooking based on food photos! 📸🍳 #AI #MiniGPT4 #Innovation

The primary reason for GPT-4's advanced multi-modal generation capabilities is the utilization of an advanced large language model.
MiniGPT-4 aligns a frozen visual encoder with a frozen LLM, Vicuna, using just one projection layer.
MiniGPT-4 exhibits capabilities similar to GPT-4, such as generating detailed image descriptions and creating websites from hand-written drafts.
Additional capabilities of MiniGPT-4 include writing stories and poems inspired by images, providing solutions to image-based problems, and teaching cooking based on food photos.
Pretraining on raw image-text pairs alone leads to unnatural language outputs lacking coherence, requiring finetuning with a well-aligned dataset using a conversational template.
The efficient model trains a projection layer with around 5 million aligned image-text pairs.
The architecture of MiniGPT-4 includes a vision encoder with ViT and Q-Former, a single linear projection layer, and the Vicuna large language model.
Training the linear layer aligns visual features with the Vicuna model in MiniGPT-4.