MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

TL;DR

MiniGPT-4 demonstrates that aligning a frozen visual encoder with an advanced large language model enables multi-modal abilities similar to GPT-4, including detailed image descriptions, creative writing, and website generation from sketches.

Contribution

This work is the first to show that proper alignment of visual features with a sophisticated LLM can unlock advanced multi-modal capabilities, bridging vision and language understanding.

Findings

MiniGPT-4 can generate detailed image descriptions and creative content.

Fine-tuning with a curated dataset improves generation quality and reliability.

The approach reveals that large language models can be effectively adapted for vision-language tasks.

Abstract

The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision-language models. However, the technical details behind GPT-4 continue to remain undisclosed. We believe that the enhanced multi-modal generation capabilities of GPT-4 stem from the utilization of sophisticated large language models (LLM). To examine this phenomenon, we present MiniGPT-4, which aligns a frozen visual encoder with a frozen advanced LLM, Vicuna, using one projection layer. Our work, for the first time, uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by GPT-4, such as detailed image description generation and website creation from hand-drawn drafts. Furthermore, we also observe other emerging capabilities in MiniGPT-4, including writing stories and poems inspired by given images, teaching users how to cook based on food photos, and so on. In our experiment, we found that the model trained on short image caption pairs could produce unnatural language outputs (e.g., repetition and fragmentation). To address this problem, we curate a detailed image description dataset in the second stage to finetune the model, which consequently improves the model's generation reliability and overall usability. Our code, pre-trained model, and collected dataset are available at https://minigpt-4.github.io/.