What to Preserve and Trust in Multi-modal Clustering: Cross-Guided Information Bottleneck with Reliable Contrastive Learning

Multi-modal clustering (MMC) aims to discover common cluster structures from multiple modalities. Effective MMC requires determining what information to preserve and how strongly negative relations are trusted. Existing information bottleneck (IB)-based MMC methods can suppress unnecessary information while preserving shared content, but fail to explicitly consider that the contribution of each modality may vary across samples. Moreover, existing contrastive MMC methods can promote cross-modal consistency, but often overlook that negative pairs may contribute differently, especially when their relations are ambiguous or the samples involved are uncertain or inconsistent across modalities. To address these issues, we propose a novel reliable multi-modal clustering framework named CGIB-RCL, consisting of Cross-Guided Information Bottleneck (CGIB) and Reliable Contrastive Learning (RCL). Specifically, CGIB constructs guidance for each target modality by adaptively combining the remaining modalities for each sample, preserving information supported across modalities while reducing modality-specific content. RCL separately models relation ambiguity and sample reliability to determine the contribution of each negative pair, reducing the influence of ambiguous relations and pairs involving unreliable samples during contrastive learning. Together, CGIB and RCL regulate what information is preserved and how strongly negative relations are trusted during learning. Extensive experiments on five datasets demonstrate the effectiveness of the proposed framework. The source code will be available on the author's GitHub homepage upon acceptance.