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
https://anonymous.4open.science/r/Explore_FGVDs-E277.

该研究使用大规模视觉语言模型 (LVLMs) 来评估它们在识别相似对象和准确描述视觉特征方面的独特性和忠实度，并提出了文本检索增强分类 (TRAC) 框架以深入分析细粒度的视觉描述生成。研究结果表明，在生成细粒度描述方面，MiniGPT-4 比其他两个模型表现更好。