Multi-modal clustering aims to integrate complementary information from different modalities to uncover latent consistent structures and improve clustering performance. However, existing methods mainly rely on predictive (result) uncertainty to improve robustness, while often neglecting aleatoric (data) uncertainty introduced by sample noise and epistemic (model) uncertainty induced by model parameters and structural variations. To this end, we propose a novel Full-Stage Trusted Multi-modal Clustering (FSTMC) method. To the best of trust, we jointly utilize aleatoric, epistemic, and predictive uncertainties to optimize the model and learn more reliable feature representations and clustering results. In the representation learning phase, probabilistic modeling is used to capture stable latent representations and estimate aleatoric uncertainty, while structured random perturbations are present to estimate epistemic uncertainty. In the clustering stage, instead of conventional feature-level fusion, we design an evidence-based fusion strategy, where soft labels from each modality are first mapped into categorical evidence while cluster distributions are parameterized via a Dirichlet model, with finally dynamic multi-modal fusion achieved by Dempster-Shafer theory. To mitigate overconfidence and modal conflicts, prior constraints guided by aleatoric and epistemic uncertainty are imposed, resulting in calibrated predictive uncertainty. Finally, we exploit predictive uncertainty to selectively incorporate pseudo labels for optimization. Benchmark experiments on a number of multi-modal datasets demonstrate that our approach significantly improves accuracy compared to state-of-the-art methods.

Multiview clustering (MVC) is a popular research topic in the fields of data mining and pattern recognition, which focuses on fully exploring and employing the correlations between views to jointly discover a consistent cluster structure across data. Typically, weighted MVC is a common clustering method aimed at learning the importance or weights of each view and applying them to explore the complementary information between views. However, current weighted MVCs primarily focus on the quality of each view while overlooking the crucial role of pseudo-label-based self-supervision in weight learning. In addition, most weighted MVCs only use a weighting mechanism to utilize complementary features without sufficiently considering the consistency relationship between the clustering results of individual views and the final clustering result. Aiming to solve the above problems, this article proposes a structure-enhanced self-supervised weighted information bottleneck (S2WIB) method for MVC. Specifically, the S2WIB method establishes a view-weight learning mechanism that leverages both the view-contained information and the self-supervised information to learn view weights, and then integrates the weighted information from different views. Meanwhile, based on the information bottleneck (IB) theory, it explores view correlations from two perspectives, namely complementary information and consistent view cluster structure information, thereby fully exploiting the potential information contained in multiview data. Experiments on various multiview text datasets, multifeature image datasets, multiangle video datasets, multimodal text-image datasets, as well as large-scale datasets and biological multiomics datasets, demonstrate that the S2WIB method performs effectively and exhibits superiority across various fields.

In an era of increasingly diverse information sources, multi-modal clustering (MMC) has become a key technology for processing multi-modal data. It can apply and integrate the feature information and potential relationships of different modalities. Although there is a wealth of research on MMC, due to the complexity of datasets, a major challenge remains in how to deeply explore the complex latent information and interdependencies between modalities. To address this issue, this paper proposes a method called super deep contrastive information bottleneck (SDCIB) for MMC, which aims to explore and utilize all types of latent information to the fullest extent. Specifically, the proposed SDCIB explicitly introduces the rich information contained in the encoder’s hidden layers into the loss function for the first time, thoroughly mining both modal features and the hidden relationships between modalities. Moreover, the proposed SDCIB performs dual optimization by simultaneously considering consistency information from both the feature distribution and clustering assignment perspectives, the proposed SDCIB significantly improves clustering accuracy and robustness. We conducted experiments on 4 multi-modal datasets and the accuracy of the method on the ESP dataset improved by 9.3%. The results demonstrate the superiority and clever design of the proposed SDCIB. The source code is available on https://github.com/ShizheHu.

Temporal sequential tasks challenge humanoid robots, as existing Diffusion Policy (DP) and Action Chunking with Transformers (ACT) methods often lack temporal context, resulting in local optima traps and excessive repetitive actions. To address these issues, this paper introduces a Classifier-Free Guidance-Based Diffusion Policy (CFG-DP), a novel framework to enhance DP by integrating Classifier-Free Guidance (CFG) with conditional and unconditional models. Specifically, CFG leverages timestep inputs to track task progression and ensure precise cycle termination. It dynamically adjusts action predictions based on task phase, using a guidance factor tuned to balance temporal coherence and action accuracy. Real-world experiments on a humanoid robot demonstrate high success rates and minimal repetitive actions. Furthermore, we assessed the model's ability to terminate actions and examined how different components and parameter adjustments affect its performance. This framework significantly enhances deterministic control and execution reliability for sequential robotic tasks.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.