Libra: Building Decoupled Vision System on Large Language Models
<|reference_start|>Libra: Building Decoupled Vision System on Large Language Models: 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.<|reference_end|>
@article{xu2024libra:,
    title={Libra: Building Decoupled Vision System on Large Language Models},
    author={Yifan Xu, Xiaoshan Yang, Yaguang Song, Changsheng Xu},
    journal={arXiv preprint arXiv:2405.10140},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10140},
    primaryClass={cs.CV}
}