Performant vision-language (VL) models like CLIP represent captions using a single vector. How much information about language is lost in this bottleneck? We first curate CompPrompts, a set of increasingly compositional image captions that VL models should be able to capture (e.g., single object, to object+property, to multiple interacting objects). Then, we train text-only recovery probes that aim to reconstruct captions from single-vector text representations produced by several VL models. This approach doesn't require images, allowing us to test on a broader range of scenes compared to prior work. We find that: 1) CLIP's text encoder falls short on object relationships, attribute-object association, counting, and negations; 2) some text encoders work significantly better than others; and 3) text-only recovery performance predicts multi-modal matching performance on ControlledImCaps: a new evaluation benchmark we collect+release consisting of fine-grained compositional images+captions. Specifically -- our results suggest text-only recoverability is a necessary (but not sufficient) condition for modeling compositional factors in contrastive vision+language models. We release data+code.

通过创建 CompPrompts 数据集的方法，研究了视觉-语言模型的编码模式对语言信息的损失情况，并提出了基于文本恢复的方法和 ControlledImCaps 评估基准，发现文本恢复能力与模型适用于呈现目标属性关系、计数、否定和多目标交互等复合要素的能力相关性强，并表明这是对偶视觉+语音模型的必要条件。

对比视觉语言模型中的文本编码器是性能瓶颈