Introduction

MiniGPT-4 represents an advanced large language model that enhances vision-language comprehension through the alignment of a fixed visual encoder with Vicuna, a large language model, utilizing a single projection layer.

Similar to GPT-4, MiniGPT-4 boasts a range of capabilities, including generating detailed descriptions of images and transforming handwritten drafts into websites.

Additionally, the tool demonstrates emerging functionalities such as crafting stories and poems based on provided images, offering solutions to problems depicted in images, and providing cooking instructions based on food photographs.

Training MiniGPT-4 involves aligning the linear layer to synchronize visual features with the Vicuna model, employing about 5 million aligned image-text pairs, ensuring highly efficient computational training.

During pretraining with raw image-text pairs, the model may initially generate unnatural language outputs characterized by repetition and fragmented sentences. To mitigate this, MiniGPT-4 utilizes a meticulously curated, well-aligned dataset for fine-tuning, leveraging a conversational template. This step significantly enhances the model's reliability in generating coherent text.

MiniGPT-4's architecture integrates a vision encoder featuring pre-trained VIT and Q-former components, a single linear projection layer, and an advanced Vicuna Large Language Model, optimizing its performance across various applications.