Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks

Xinsong Zhang, Yan Zeng, Jipeng Zhang, Hang Li

Abstract
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. Code and pre-trained models are released at https://github.com/zhangxinsong-nlp/XFM.
Anthology ID:
2023.findings-emnlp.40
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
551–568
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.40/
DOI:
10.18653/v1/2023.findings-emnlp.40
Bibkey:
Cite (ACL):
Xinsong Zhang, Yan Zeng, Jipeng Zhang, and Hang Li. 2023. Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 551–568, Singapore. Association for Computational Linguistics.
Cite (Informal):
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks (Zhang et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.40.pdf
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