Interpretable Text-Guided Image Clustering via Iterative Search


Bingchen Zhao (University of Edinburgh), Oisin Mac Aodha (University of Edinburgh)
The 35th British Machine Vision Conference
PDFPosterSupplementary

Abstract

Traditional clustering methods aim to group unlabeled data points based on their similarity to each other. However, clustering, in the absence of additional information, is an ill-posed problem as there may be many different, yet equally valid, ways to partition a dataset. Different users may want to use different criteria to form clusters in the same data, eg shape vs color. Recently introduced text-guided image clustering methods aim to address this ambiguity by allowing users to specify the criteria of interest using natural language instructions. This instruction provides the necessary context and control needed to obtain clusters that are more aligned with the users' intent. We propose a new text-guided clustering approach named ITGC that uses an iterative discovery process, guided by an unsupervised clustering objective, to generate interpretable visual concepts that better capture the criteria expressed in a user's instructions. We report superior performance compared to existing methods across a wide variety of image clustering and fine-grained classification benchmarks.

Citation

@inproceedings{Zhao_2025_BMVC,
author    = {Bingchen Zhao and Oisin Mac Aodha},
title     = {Interpretable Text-Guided Image Clustering via Iterative Search},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_768/paper.pdf}
}


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