Aligned text-image encoders such as CLIP have become the de facto model for
vision-language tasks. Furthermore, modality-specific encoders achieve
impressive performances in their respective domains. This raises a central
question: does an alignment exist between uni-modal vision and language
encoders since they fundamentally represent the same physical world? Analyzing
the latent spaces structure of vision and language models on image-caption
benchmarks using the Centered Kernel Alignment (CKA), we find that the
representation spaces of unaligned and aligned encoders are semantically
similar. In the absence of statistical similarity in aligned encoders like
CLIP, we show that a possible matching of unaligned encoders exists without any
training. We frame this as a seeded graph-matching problem exploiting the
semantic similarity between graphs and propose two methods - a Fast Quadratic
Assignment Problem optimization, and a novel localized CKA metric-based
matching/retrieval. We demonstrate the effectiveness of this on several
downstream tasks including cross-lingual, cross-domain caption matching and
image classification.

通过使用 Centered Kernel Alignment (CKA) 分析图像字幕基准上视觉和语言模型的潜在空间结构，我们发现不对齐和对齐的编码器的表示空间在语义上是相似的。在无统计相似性的情况下，我们展示了存在可能匹配不对齐编码器而无需任何训练。我们将其视为一种基于种子图匹配问题，利用图之间的语义相似性提出了两种方法 - 一种是快速二次分配问题优化，一种是基于新颖局部 CKA 度量的匹配 / 检索。我们在包括跨语言、跨域字幕匹配和图像分类在内的几个下游任务上展示了其有效性。