Official implementation of "Describing Sets of Images with Textual-PCA".
Abstract: 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.
Sample radar plots, in which the value of the projection to the principle phrases are depicted. In blue we show positive correlation and in red negative correlation.
$ python run_textual_pca.py
--number_principal_phrases 7
--variance_coeff 10.0
--logit_coeff 8.0
--prev_phrases_dist_coeff -80.0
--dataset_path "./datasets/custom_dataset" If you found this code useful, we would appreciate you citing the related paper
@article{hupert2022describing,
title={Describing Sets of Images with Textual-PCA},
author={Hupert, Oded and Schwartz, Idan and Wolf, Lior},
journal={arXiv preprint arXiv:2210.12112},
year={2022}
}