  Foundation models or pre-trained models have substantially improved the
performance of various language, vision, and vision-language understanding
tasks. However, existing foundation models can only perform the best in one
type of tasks, namely language, vision, or vision-language. It is still an open
question whether it is possible to construct a foundation model performing the
best for all the understanding tasks, which we call a general foundation model.
In this paper, we propose a new general foundation model, X-FM (the
X-Foundation Model). X-FM has one language encoder, one vision encoder, and one
fusion encoder, as well as a new training method. The training method includes
two new techniques for learning X-FM from text, image, and image-text pair
data. One is to stop gradients from the vision-language training when learning
the language encoder. The other is to leverage the vision-language training to
guide the learning of the vision encoder. Extensive experiments on benchmark
datasets show that X-FM can significantly outperform existing general
foundation models and perform better than or comparable to existing foundation
models specifically for language, vision, or vision-language understanding.
Code and pre-trained models are released at
https://github.com/zhangxinsong-nlp/XFM.
