Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic text-image alignment evaluation. We first introduce SeeTRUE: a comprehensive evaluation set, spanning multiple datasets from both text-to-image and image-to-text generation tasks, with human judgements for whether a given text-image pair is semantically aligned. We then describe two automatic methods to determine alignment: the first involving a pipeline based on question generation and visual question answering models, and the second employing an end-to-end classification approach by finetuning multimodal pretrained models. Both methods surpass prior approaches in various text-image alignment tasks, with significant improvements in challenging cases that involve complex composition or unnatural images. Finally, we demonstrate how our approaches can localize specific misalignments between an image and a given text, and how they can be used to automatically re-rank candidates in text-to-image generation.

翻译：自动判断文本与对应图像是否在语义上对齐，是视觉语言模型面临的重要挑战，其在文本到图像生成以及图像到文本生成任务中具有广泛应用。本文研究图文对齐的自动评估方法。首先，我们引入 SeeTRUE：一个覆盖文本到图像和图像到文本生成任务中多个数据集的综合评估集，其中包含人类对给定图文对是否语义对齐的判断。接着，我们描述两种自动判断对齐的方法：第一种基于问题生成和视觉问答模型的流水线方法，第二种通过微调多模态预训练模型采用端到端分类方法。两种方法在多种图文对齐任务中均优于先前方法，在涉及复杂构图或非自然图像的困难案例中表现尤为显著。最后，我们展示如何利用所提方法定位图像与给定文本之间的特定不匹配情况，以及如何将其用于自动重新排序文本到图像生成中的候选结果。