ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced
  MiniGPT-4
Authors: name: Zhengqing Yuan, name: Huiwen Xue, name: Xinyi Wang, name: Yongming Liu, name: Zhuanzhe Zhao, name: Kun Wang

In recent years, large language models (LLMs) have made significant progress
in natural language processing (NLP), with models like ChatGPT and GPT-4
achieving impressive capabilities in various linguistic tasks. However,
training models on such a large scale is challenging, and finding datasets that
match the model's scale is often difficult. Fine-tuning and training models
with fewer parameters using novel methods have emerged as promising approaches
to overcome these challenges. One such model is MiniGPT-4, which achieves
comparable vision-language understanding to GPT-4 by leveraging novel
pre-training models and innovative training strategies. However, the model
still faces some challenges in image understanding, particularly in artistic
pictures. A novel multimodal model called ArtGPT-4 has been proposed to address
these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100
device in just 2 hours, using only about 200 GB of data. The model can depict
images with an artistic flair and generate visual code, including aesthetically
pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks
for evaluating the performance of vision-language models. In the subsequent
evaluation methods, ArtGPT-4 scored more than 1 point higher than the current
state-of-the-art model and was only 0.25 points lower than artists on
a 6-point scale. Our code and pre-trained model are available at
https://huggingface.co/Tyrannosaurus/ArtGPT-4.