DD-FSC: Dirichlet Process-Guided Diffusion Model for Few-Shot Clustering

In few-shot deep clustering, partial category coverage and an unknown number of latent categories make it difficult to propagate sparse semantic supervision while reliably recovering the complete clustering structure. To address this challenge, we propose DD-FSC, a nonparametric few-shot clustering framework that jointly models feature-space density and latent cluster structure. DD-FSC characterizes the feature distribution with a diffusion model and employs score-guided Langevin dynamics to propagate limited supervision toward high-density semantic regions, thereby identifying high-confidence same-category samples and constructing density-aware positive pairs. Meanwhile, Dirichlet process (DP) inference models the evolving cluster structure and adaptively determines the effective number of clusters without requiring it to be specified in advance. Theoretically, we show that the deterministic drift of score-guided Langevin dynamics is positively aligned with the modeled log-density gradient, providing a principled basis for density-guided semantic propagation. Through this density-guided semantic expansion and adaptive structure inference, DD-FSC extends sparse supervision beyond the observed categories while recovering the underlying clustering structure. Extensive experiments demonstrate superior clustering performance under both known- and unknown-cluster-number settings, together with accurate estimation of the latent category count.