Abstract
In this work, we study idiosyncrasies in the caption models and their downstream impact on text-to-image models. We design a systematic analysis: given either a generated caption or the corresponding image, we train neural networks to predict the originating caption model. Our results show that text classification yields very high accuracy (99.70\%), indicating that captioning models embed distinctive stylistic signatures. In contrast, these signatures largely disappear in the generated images, with classification accuracy dropping to at most 50\% even for the state-of-the-art Flux model. To better understand this cross-modal discrepancy, we further analyze the data and find that the generated images fail to preserve key variations present in captions, such as differences in the level of detail, emphasis on color and texture, and the distribution of objects within a scene. Overall, our classification-based framework provides a novel methodology for quantifying both the stylistic idiosyncrasies of caption models and the prompt-following ability of text-to-image systems.
Abstract (translated)
在这项工作中，我们研究了描述模型（caption models）的特性及其对文本到图像生成模型的影响。设计了一套系统的分析方法：给定由某特定模型生成的文字说明或对应图片，训练神经网络预测其使用的原始描述模型。实验结果显示，基于文字分类的方法能够达到非常高的准确率（99.70%），表明不同的描述模型具有独特的风格特征。然而，这些独特标志在生成的图像中却几乎消失不见，即便对于最先进的Flux模型，通过图片进行分类的准确性也降至最多50%。 为了更好地理解跨模态差异的原因，我们进一步分析了数据集，发现生成的图片未能保留文字说明中的关键变化，例如细节程度、对颜色和纹理的关注以及场景中物体分布的不同。总体而言，我们的基于分类的方法为量化描述模型的独特风格特征及文本到图像系统遵循提示的能力提供了一种新颖且具有创新性的方法论。
URL
https://arxiv.org/abs/2602.22734
