Image Clustering Conditioned on Text Criteria

Sehyun Kwon, Jaeseung Park, Minkyu Kim, Jaewoong Cho, Ernest K. Ryu, Kangwook Lee

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Keywords: image clustering, vision-language models, large language models, foundation models

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TL;DR: We propose a novel image clustering method that perform clustering based on a user-specified criterion.

Abstract: Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a user has in mind. In this work, we present a new methodology for performing image clustering based on user-specified criteria in the form of text by leveraging modern Vision-Language Models and Large Language Models. We call our method Image Clustering Conditioned on Text Criteria (IC$|$TC), and it represents a different paradigm of image clustering. IC$|$TC requires a minimal and practical degree of human intervention and grants the user significant control over the clustering results in return. Our experiments show that IC$|$TC can effectively cluster images with various criteria, such as human action, physical location, or the person's mood, significantly outperforming baselines.

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Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Submission Number: 6858