  Image clustering divides a collection of images into meaningful groups,
typically interpreted post-hoc via human-given annotations. Those are usually
in the form of text, begging the question of using text as an abstraction for
image clustering. Current image clustering methods, however, neglect the use of
generated textual descriptions. We, therefore, propose Text-Guided Image
Clustering, i.e., generating text using image captioning and visual
question-answering (VQA) models and subsequently clustering the generated text.
Further, we introduce a novel approach to inject task- or domain knowledge for
clustering by prompting VQA models. Across eight diverse image clustering
datasets, our results show that the obtained text representations often
outperform image features. Additionally, we propose a counting-based cluster
explainability method. Our evaluations show that the derived keyword-based
explanations describe clusters better than the respective cluster accuracy
suggests. Overall, this research challenges traditional approaches and paves
the way for a paradigm shift in image clustering, using generated text.
