Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks
Xinsong Zhang, Yan Zeng, Jipeng Zhang, Hang Li · 2023
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 general foundation model performing the best for all the understanding tasks.In this paper, we propose a new method for training the 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.