Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models
<|reference_start|>Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models: Large Vision-Language Models (LVLMs) are gaining traction for their remarkable ability to process and integrate visual and textual data. Despite their popularity, the capacity of LVLMs to generate precise, fine-grained textual descriptions has not been fully explored. This study addresses this gap by focusing on \textit{distinctiveness} and \textit{fidelity}, assessing how models like Open-Flamingo, IDEFICS, and MiniGPT-4 can distinguish between similar objects and accurately describe visual features. We proposed the Textual Retrieval-Augmented Classification (TRAC) framework, which, by leveraging its generative capabilities, allows us to delve deeper into analyzing fine-grained visual description generation. This research provides valuable insights into the generation quality of LVLMs, enhancing the understanding of multimodal language models. Notably, MiniGPT-4 stands out for its better ability to generate fine-grained descriptions, outperforming the other two models in this aspect. The code is provided at \url{https://anonymous.4open.science/r/Explore_FGVDs-E277}.<|reference_end|>
@article{huang2024exploring,
    title={Exploring the Distinctiveness and Fidelity of the Descriptions Generated
  by Large Vision-Language Models},
    author={Yuhang Huang, Zihan Wu, Chongyang Gao, Jiawei Peng and Xu Yang},
    journal={arXiv preprint arXiv:2404.17534},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17534},
    primaryClass={cs.CV cs.MM}
}