Uncertainty-Driven Trustworthy Multi-modal Information Bottleneck Clustering

The Information Bottleneck (IB) theory seeks to extract minimal sufficient representations and filter redundant noise, providing a solid foundation for multi-modal clustering. Although traditional IB-based methods demonstrate significant advantages, they still face several challenges. These include the inability to effectively model modality uncertainty, the tendency to yield overconfident incorrect predictions in the presence of modality conflicts, and the disconnection between representation learning and the final decision-making process. To address these issues, we propose a novel clustering method, the UNcertainty-drIven Trustworthy Multi-modal Information Bottleneck (UNIT-MIB). This approach adaptively regulates compression intensity based on uncertainty estimations and employs a dual-path fusion strategy. Specifically, it integrates semantic features via uncertainty-weighted aggregation and uncertainty-aware opinions via the Dempster-Shafer theory. Furthermore, based on Deng entropy, we formulate a Deng mutual information measure to characterize the discrepancy between semantic and evidential opinions, and introduce a stop-gradient-based directed alignment mechanism to impose asymmetric optimization. This mechanism steers semantic representation learning toward trustworthy evidential decision-making, thereby bridging the semantic-logical gap. Experimental results demonstrate that the proposed UNIT-MIB significantly outperforms state-of-the-art methods in terms of clustering accuracy across multiple real-world datasets.