Although multiview learning methods have been widely studied, they mostly focus on improving accuracy while ignoring decision uncertainty. In the real world, multiview data often encounters misalignment issues, resulting in conflictive instances and further limiting the application of these methods in safety-critical domains. Recently, some efforts have been made to improve the reliability of multiview learning methods by estimating decision uncertainty, but most methods often experience performance degradation due to their inability to handle conflictive instances. To address this issue, we propose a Robust Trusted Conflictive Multiview Collaborative Contrastive Learning (RCMCL) method, which enhances the model's robustness and generalization ability in conflictive multiview scenarios. Specifically, RCMCL first uses an evidential deep neural network to construct view-specific opinions, and then employs dissonance-based evidence contrastive learning to enhance the consistency of these opinions across different views. Subsequently, RCMCL performs collaborative learning of consistent evidence and complementary evidence. It first introduces the vacuity degree into the complementary evidence to extract more useful information, and then employs category-level contrastive learning to separate consistent and complementary evidence. In addition, consistent and complementary evidence is combined to make a joint decision. Finally, experimental results on eight benchmark datasets verify the superiority of RCMCL over state-of-the-art methods.

Accurate brain tumor segmentation from multimodal MRI is critical for clinical diagnosis and treatment planning. However, effectively leveraging the complementary information across different modalities remains a significant challenge due to modality-specific noise, information redundancy and inherent model uncertainty. To tackle these challenges, we propose a Disentangled Fusion Network (DFuse-Net) that integrates disentangled feature fusion with contrastive and consistency-aware learning to enable reliable multi-modal brain tumor segmentation. Our method first explicitly disentangles modality-shared and modality-specific feature representations. Then, a Disentangled Feature Fusion Module (DFFM) is proposed to effectively integrate modality-shared and modalityspecific feature representations. In addition, a contrastive-aware learning scheme is employed to enhance feature discriminability, while a consistency-aware learning strategy is applied to enforce structural coherence across modalities. Moreover, Monte Carlo dropout is applied during inference to generate voxelwise aleatoric and epistemic uncertainty maps, enhancing the robustness of segmentation. Extensive experiments on the BraTS datasets demonstrate that DFuse-Net achieves superior segmentation accuracy and reliability compared to the state-of-the-art methods.

Multi-View Clustering (MVC) aims to mine complementary information across different views to partition multi-view data more effectively and has attracted considerable interest. However, existing deep multi-view clustering methods frequently neglect the exploration of structural information within individual view and lack the learning of structural consistency among views, which results in limitations in the clustering performance. In this paper, we introduce a novel multi-view clustering framework based on graph consistency learning to address this issue. Specifically, we design intra-view graph contrastive learning to uncover structural information within each view and achieve structural conscistency objectives through cross-view graph consistency learning. Additionally, to address the conflict between different learning objectives when trained in the same space, we introduce two new feature spaces, one for cluster-levcel contrastive learning and the other for instance-level contrastive learning. Subsequently, to make the most of discriminative information from all views, we concatenate high-level features from all views to form global features and employ self-supervision to promote clustering consistency across different views. Experimental results on several challenging datasets demonstrate the outstanding performance of our proposed method.

Multi-grained cross-modal image-text retrieval models have demonstrated promising outcomes through the alignment of local and global features. However, this advancement often results in larger model sizes and higher computational requirements, which raises concerns regarding the balance between performance and efficiency. To address this challenge, we introduce a novel lightweight multi-grained (LMG) image-text retrieval paradigm aimed at enhancing model efficiency. Specifically, in our approach, we first re-frame the retrieval problem as a cascaded representation learning task. This involves leveraging only fine-grained features to capture coarse-grained constraints, thereby reducing computational burden while maintaining accuracy. Furthermore, we replace computationally expensive parametric feature aggregation methods with three efficient parameter-free alternatives: auto-correlation matrix, discrete linear convolution, and discrete Fourier transform. The proposed LMG model is extensively compared with state-of-the-art approaches on two benchmark datasets, i.e., Flickr30K and MSCOCO, and the experimental results highlight the superior performance of LMG. Additionally, we explore the impact of different feature aggregation methods on LMG and conduct a sensitivity analysis on the coarse and fine-grained constraints ratio hyper-parameter.

Graph-based multiview clustering methods have attracted much attention because of their ability to mine nonlinear structural information among instances. Although they perform well in many scenarios, they consume a lot of computational resources when dealing with large-scale multiview scenarios. To address this issue, we present a new insight into the anchor graph mechanism and propose a novel Nonnegative Anchor Graph Reconstruction (NAGR) model. NAGR introduces the sparse similarity graph into the symmetric matrix factorization and gets the nonnegative representation that retains the graph structural information. Thereafter, we develop a novel Efficient Multiview nonnegative Representation learning framework with Correntropy and Anchor graph (EMR-CA), which integrates multiview anchor graph reconstruction and consensus nonnegative representation learning into a unified framework. EMR-CA uses multiview anchor graph reconstruction to learn consensus nonnegative representation, where correntropy rather than F-norm is used as the approximation measurement criterion. Specifically, normalized anchor graphs of different views are decomposed into a consensus nonnegative representation and multiple view-specific representations, where the consensus representation retains the neighbor graph information between multiview instances and representative anchors on different views. Finally, the effectiveness of the proposed EMR-CA framework is verified by theoretical analysis and experimental results on large-scale realistic multiview scenarios.

Despite incomplete multiview clustering (IMC) being widely studied in the past decade, it is still difficult to model the correlation among multiple views due to the absence of partial views. Most existing works for IMC only mine the correlation among multiple views from available views and ignore the importance of missing views. To address this issue, we propose a novel Incomplete Multiview Nonnegative representation learning model with Graph completion and Adaptive neighbors (IMNGA), which performs common graph learning, missing graph completion, and consensus nonnegative representation learning simultaneously. In IMNGA, the common graph on all views and the incomplete graph of each view are used to reconstruct the completed graph of the corresponding view, where the common graph satisfies the neighbor constraints of incomplete multiview data and consensus representation. IMNGA gets consensus representation by factorizing completed and incomplete graphs, where consensus representation satisfies the common graph constraint. IMNGA shows its effectiveness by outperforming other state-of-the-art methods.

Multivariate time series clustering has become an important research topic in the time series learning task, which aims to discover the correlation among multiple sequences and partition multivariate time series data into several subsets. Although there are currently some methods that can handle this task, most of them fail to discover informative subsequences from multivariate time series instances. In this paper, we first propose a novel unsupervised shapelet learning with adaptive neighbors (USLA) model for learning salient multivariate subsequences (i.e., multivariate shapelets), where the importance of each variate can be auto-determined when given a candidate multivariate shapelet. USLA performs multivariate shapelet-transformed representation learning and local structure learning simultaneously, but the performance of USLA with multivariate shapelets of different lengths is comparable to that of isometric multivariate shapelets. In fact, the shapelet-transformed representations learned from multivariate shapelets of different lengths can all represent multivariate time series instances separately and often contain complementary information to each other. Therefore, we develop a novel multiview USLA (MUSLA) model which treats shapelet-transformed representations learned from shapelets of different lengths as different views. In this way, MUSLA learns the importance of each view and the neighbor graph matrix among multiview representations when candidate multivariate shapelets of different lengths are determined. Experimental results show that MUSLA outperforms other state-of-the-art multivariate time series algorithms on real-world multivariate time series datasets.

Recently, the restricted Boltzmann machine (RBM) has aroused considerable interest in the multiview learning field. Although effectiveness is observed, like many existing multiview learning models, multiview RBM ignores the local manifold structure of multiview data. In this article, we first propose a novel graph RBM model, which preserves the data manifold structure and is amenable to Gibbs sampling. Then, we develop a multiview graph RBM model on the basis of the graph RBM, which performs local structural learning and multiview representation learning simultaneously. The proposed multiview model has the following merits: 1) it preserves the data manifold structure for multiview classification and 2) it performs view-consistent representation learning and view-specific representation learning simultaneously. The experimental results show that the proposed multiview model outperforms other state-of-the-art multiview classification algorithms.