Granular Information Bottleneck for Deep Multi-Modal Clustering

Deep multi-modal clustering generally focuses on improving clustering accuracy by leveraging information from different modalities. However, existing methods are designed around the finest-grained points as input, neglecting the relationships and information integration across different granularity levels, which negatively affects the clustering results. To this end, we propose a novel granular information bottleneck (GIB) for deep multi-modal clustering, which embeds a dual-tiered information bottleneck constraint mechanism that operates synergistically at both granular and sample levels, thereby learning discriminative feature representations with enhanced inter-cluster separability. Specifically, GIB adaptively represents and covers the sample points through granular balls of different granularity levels, which effectively captures the feature distribution within each cluster. Simultaneously, information compression and preservation are used to exploit the independence and complementarity of modalities while optimizing cluster assignments alignment. Finally, the objectives of GIB are formulated as a target function based on mutual information, and we propose a variational optimization method to ensure its convergence. Extensive experimental results validate the effectiveness of the proposed GIB model in accuracy and reliability.