ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced
  MiniGPT-4
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.
6461879a933afb0106ab7fef
Zhengqing Yuan
admin_assigned
2023-06-02T12:54:49.864Z
62f3cc40b3e6016d46027e1d
/avatars/9b26d2fd5e8574f633ffd97af1700b45.svg
YuanZhengqing
user
YuanZhengqing
6461879a933afb0106ab7ff0
Huiwen Xue
admin_assigned
2023-06-02T12:55:18.865Z
63ef336ed0f7b3b85ac10cbc
https://cdn-avatars.huggingface.co/v1/production/uploads/1677037921739-63ef336ed0f7b3b85ac10cbc.jpeg
xuehuiwen
user
SmellStone
6461879a933afb0106ab7ff1
Xinyi Wang
6461879a933afb0106ab7ff2
Yongming Liu
6461879a933afb0106ab7ff3
Zhuanzhe Zhao
6461879a933afb0106ab7ff4
Kun Wang
ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced
  MiniGPT-4
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.
ArtGPT-4, a novel multimodal model, achieves high performance in vision-language tasks and artistic image depiction using limited data and time.
large language models LLMs natural language processing NLP ChatGPT GPT-4 fine-tuning parameter-efficient fine-tuning MiniGPT-4 vision-language understanding multimodal model pre-training models ArtGPT-4 image-text pairs artistic pictures visual code HTML/CSS benchmarks state-of-the-art