标题： CapText：基于大型语言模型的图像上下文和描述的标题生成 显示英文标题

标题： CapText: Large Language Model-based Caption Generation From Image Context and Description

摘要： 虽然深度学习模型在图像到文本数据集上已被证明表现良好，但在实际中用于图像描述却很困难。 这是因为传统上，描述通常依赖于上下文，并提供关于图像的补充信息，而模型往往生成描述图像视觉特征的描述。 在图像描述生成的先前研究中，探索了在提供图像及其相应描述或上下文时生成描述的模型的使用。 我们提出并评估了一种新方法，该方法利用现有的大型语言模型仅从文本描述和上下文生成描述，而无需直接处理图像。 我们证明，在微调后，我们的方法在CIDEr指标上优于当前最先进的图像-文本对齐模型如OSCAR-VinVL。 显示英文摘要

摘要： While deep-learning models have been shown to perform well on image-to-text datasets, it is difficult to use them in practice for captioning images. This is because captions traditionally tend to be context-dependent and offer complementary information about an image, while models tend to produce descriptions that describe the visual features of the image. Prior research in caption generation has explored the use of models that generate captions when provided with the images alongside their respective descriptions or contexts. We propose and evaluate a new approach, which leverages existing large language models to generate captions from textual descriptions and context alone, without ever processing the image directly. We demonstrate that after fine-tuning, our approach outperforms current state-of-the-art image-text alignment models like OSCAR-VinVL on this task on the CIDEr metric.