Libra: Building Decoupled Vision System on Large Language Models
Yifan Xu, Xiaoshan Yang, Yaguang Song, Changsheng Xu
Abstract
In this work, we introduce **Libra**, a prototype model with a decoupled vision system on a large language model (LLM). The decoupled vision system decouples inner-modal modeling and cross-modal interaction, yielding unique visual information modeling and effective cross-modal comprehension. Libra is trained through discrete auto-regressive modeling on both vision and language inputs. Specifically, we incorporate a routed visual expert with a cross-modal bridge module into a pretrained LLM to route the vision and language flows during attention computing to enable different attention patterns in inner-modal modeling and cross-modal interaction scenarios. Experimental results demonstrate that the dedicated design of Libra achieves a strong MLLM baseline that rivals existing works in the image-to-text scenario with merely 50 million training data, providing a new perspective for future multimodal foundation models. Code is available at https://github.com/YifanXu74/Libra.
BibTeX
@inproceedings{
xu2024libra,
title={Libra: Building Decoupled Vision System on Large Language Models},
author={Yifan Xu and Xiaoshan Yang and Yaguang Song and Changsheng Xu},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=F1drhMjN7s}
}