While existing image-text alignment models reach high quality binary
assessments, they fall short of pinpointing the exact source of misalignment.
In this paper, we present a method to provide detailed textual and visual
explanation of detected misalignments between text-image pairs. We leverage
large language models and visual grounding models to automatically construct a
training set that holds plausible misaligned captions for a given image and
corresponding textual explanations and visual indicators. We also publish a new
human curated test set comprising ground-truth textual and visual misalignment
annotations. Empirical results show that fine-tuning vision language models on
our training set enables them to articulate misalignments and visually indicate
them within images, outperforming strong baselines both on the binary alignment
classification and the explanation generation tasks. Our method code and human
curated test set are available at: this https URL

本文提出了一种方法，利用大型语言模型和视觉定位模型自动构建训练集来提供检测到的文本和图像对之间的详细文本和视觉错位的解释，实验证明在我们的训练集上微调视觉语言模型可以在二元对齐分类和解释生成任务上优于强基线模型。