Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models
<|reference_start|>Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models: State-of-the-art Large Multi-Modal Models (LMMs) have demonstrated exceptional capabilities in vision-language tasks. Despite their advanced functionalities, the performances of LMMs are still limited in challenging scenarios that require complex reasoning with multiple levels of visual information. Existing prompting techniques for LMMs focus on either improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination in LMMs. In this work, we propose Scaffold prompting that scaffolds coordinates to promote vision-language coordination. Specifically, Scaffold overlays a dot matrix within the image as visual information anchors and leverages multi-dimensional coordinates as textual positional references. Extensive experiments on a wide range of challenging vision-language tasks demonstrate the superiority of Scaffold over GPT-4V with the textual CoT prompting. Our code is released in https://github.com/leixy20/Scaffold.<|reference_end|>
@article{lei2024scaffolding,
    title={Scaffolding Coordinates to Promote Vision-Language Coordination in Large
  Multi-Modal Models},
    author={Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li and Yang Liu},
    journal={arXiv preprint arXiv:2402.12058},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12058},
    primaryClass={cs.CV cs.CL}
}