How does LLaVA differ from MiniGPT-4?

LLaVA and MiniGPT-4 are both multimodal models that combine image and text processing, but they differ in architecture and goals.

• MiniGPT-4 is a simplified version of GPT-4, adapted for multimodal input. It uses a pretrained language model (e.g., LLaMA) and a visual encoder (e.g., CLIP or ViT), connected through adapters to understand and generate text based on images. MiniGPT-4 aims to create a compact and accessible model, similar in functionality to GPT-4.

• LLaVA (Large Language and Vision Assistant) is a model that more deeply integrates visual information with a language model, trained on a large number of image-text pairs with instructions. LLaVA focuses on performing complex multimodal tasks with instructions, enhancing interaction and understanding of context.

Key differences:

• LLaVA is trained with instructions and oriented towards interactive communication, while MiniGPT-4 is focused on generating text from images.
• Architectural details and methods of integrating visual and language components differ.

Thus, LLaVA is more oriented towards assistant tasks with multimodal input, while MiniGPT-4 is aimed at compact multimodal text generation, similar to GPT-4.