  We seek to semantically describe a set of images, capturing both the
attributes of single images and the variations within the set. Our procedure is
analogous to Principle Component Analysis, in which the role of projection
vectors is replaced with generated phrases. First, a centroid phrase that has
the largest average semantic similarity to the images in the set is generated,
where both the computation of the similarity and the generation are based on
pretrained vision-language models. Then, the phrase that generates the highest
variation among the similarity scores is generated, using the same models. The
next phrase maximizes the variance subject to being orthogonal, in the latent
space, to the highest-variance phrase, and the process continues. Our
experiments show that our method is able to convincingly capture the essence of
image sets and describe the individual elements in a semantically meaningful
way within the context of the entire set. Our code is available at:
https://github.com/OdedH/textual-pca.
