From Learning to Utilization: Better Exploiting External Knowledge for Image Clustering

Unsupervised image clustering increasingly leverages external knowledge, including textual vocabularies, LLM-generated descriptions, and spatial priors. However, existing studies mainly focus on how to learn useful external knowledge into image representations, while overlooking whether the learned knowledge is effectively utilized in the final cluster assignments. In this work, we identify a previously underexplored learning-utilization gap: useful category-related knowledge may be encoded in the representation but remain underutilized in clustering predictions. We formalize learning failure, utilization failure, and their gap from an information-theoretic perspective, and further show that distance-based structural relations with correctly predicted neighbors can recover underutilized knowledge and correct erroneous predictions. Guided by this theory, we propose CARE. CARE first establishes stable initial cluster assignments from externally enhanced representations through iterative pseudo-label optimization, then evaluates sample-level knowledge-utilization reliability, and finally preserves reliable predictions while adaptively exploiting local feature-space structure to refine unreliable ones. In this way, CARE progressively better aligns cluster assignments with the category-related knowledge encoded in the learned representation. Extensive experiments on 6 widely used benchmark datasets and 3 more challenging datasets, covering 3 different types of external auxiliary information, demonstrate that CARE improves clustering assignments. The code will be published.