Multi-View Clustering (MVC) aims to enhance clustering performance by integrating multi-source complementary information. However, existing deep MVC methods face inherent challenges in balancing the learning of shared consensus representations with the preservation of view-specific information: independent encoders hinder effective cross-view collaboration, while a single shared encoder tends to sacrifice representation diversity. Although the recently introduced Mixture-of-Experts (MoE) model offers a novel approach to facilitating view collaboration, its flattened expert pool design often leads to entanglement between shared and specific information, and its routing mechanism limits collaboration potential by neglecting cross-view context. To address these challenges, this paper proposes a novel deep multi-view clustering framework—Decoupled Mixture-of-Experts with Context-Aware Routing for Multi-View Clustering (DMCAR-MVC). At its core is an innovative Decoupled MoE (D-MoE) architecture. We establish a public expert pool to learn cross-view shared representations while equipping each view with an independent private expert pool to capture its unique information, thereby structurally enforcing the decoupling of shared and specific representations. Building on this, we further design a Context-Aware Hierarchical Routing (CAHR) mechanism. When routing for the public expert pool, this mechanism introduces a global context vector to guide expert selection, enabling more efficient and globally informed cross-view collaboration. Finally, to optimize the model, we adopt a multi-level contrastive learning paradigm: on one hand, a cross-view alignment loss ensures semantic consistency in shared representations; on the other, an orthogonality constraint is imposed to further enhance separability between shared and specific representations. Extensive experiments on multiple benchmark datasets demonstrate that DMCAR-MVC significantly outperforms state-of-the-art methods across key clustering metrics. Additionally, comprehensive ablation studies thoroughly validate the effectiveness and necessity of each proposed component.

Multi-view clustering aims to uncover shared semantics and complementary information across different views. However, the inherent heterogeneity among views poses significant challenges to effective collaborative modeling and information integration. While recent studies have introduced distillation-based mechanisms to enhance cross-view consistency and alleviate heterogeneity, these approaches often rely on manually defined knowledge transfer paths or fixed fusion weights, which are inflexible in handling complex and dynamic view relationships in practice. To address this issue, we propose HOARD: a novel framework for Hierarchical crOss-view Alignment for multi-view clusteRing via Decoupled information distillation. HOARD structurally decouples multi-view representations into shared and specific components, and performs hierarchical alignment. Specifically, we introduce a granular-ball contrastive alignment to enhance the semantic consistency of shared features, and a prototype collaborative transmission alignment strategy to align specific features while preserving view-specific structural characteristics. Moreover, we design an information distillation unit to adaptively model cross-view knowledge transfer in both feature spaces. An attention mechanism is further employed to integrate shared and specific information. Extensive experiments on benchmark datasets demonstrate that HOARD significantly improves alignment quality and clustering performance, achieving state-of-the-art results.

Multi-view clustering (MVC) for remote sensing data is a critical and challenging task in Earth observation. Although recent advances in graph neural network (GNN)-based MVC have shown remarkable success, the most prevalent approaches have two major limitations: 1) heavily relying on a predefined yet fixed graph, which limits the performance of clustering because the large number of indistinguishable background samples contained in remote sensing data would introduce noise information and increase structure heterogeneity; 2) ignoring the effect of confusing samples on cluster structure compactness, which leads to fluffy cluster structure and decrease feature discriminability. To address these issues, we propose a Structure-Adaptive Multi-View Graph Clustering method named SAMVGC on remote sensing data which boosts the structure homogeneity and cluster compactness by adaptively learning the graph and cluster structures, respectively. Concretely, we use the geometric structure within the feature embedding space to refine adjacency matrices. The adjacency matrices are dynamically fused with the previous ones to improve the homogeneity and stability of structure information. Additionally, the samples are separated into two categories, including the central (intra-cluster center samples) and the confusing (inter-cluster boundary samples). On the basis, we deploy the contrastive learning paradigm on the central samples within views and the consistent learning paradigm on the confusing samples between views, improving the cluster compactness and consistency. Finally, we conduct extensive experiments on four benchmarks and achieve promising results, well demonstrating the effectiveness and superiority of the proposed method.

Multi-view clustering (MVC) has emerged as a leading paradigm in unsupervised learning, gaining significant attention. Central to this framework is the concept of view-pair contrastive learning, which aims to maximize mutual information between pairs of views, thereby facilitating consistent latent representations. Nevertheless, two critical challenges remain: i) Identifying the most suitable pairs of views for contrastive learning becomes difficult when more than two views are available, especially in the absence of prior knowledge; ii)Including all available views in contrastive learning can degrade performance due to the presence of low-quality views. To address these issues, we propose a novel mechanism, EASEMVC(Efficient DuAl Selection MEchanism for Deep Multi-View Clustering). EASEMVC begins by constructing a view graph using the Optimal Transport (OT) distance between bipartite graphs of individual views. A view selection module is then designed to perform efficient view-level selection based on the topological relationships within the view graph. Additionally, a cross-view sample graph is built at the sample level, where the topological relationships among samples are used to generate reliable learning weights. Leveraging the selected view pairs and sample weights, contrastive learning is employed to obtain consistent representations across views. Extensive experiments across six benchmark datasets demonstrate that EASEMVC outperforms current state-of-the-art methods.

Real-world video-based vision systems frequently suffer concurrent degradations caused by unpredictable weather conditions such as rain, haze, and snow, severely affecting the visual quality of both human observers and the downstream computer vision tasks. In this paper, we propose All In Mamba Video Restoration (AIM-VR) model, a novel multi-degradation video restoration framework based on the Selective State Space Model. The proposed AIM-VR model effectively handles adverse weather conditions through three key innovations: a Dual-Path Mamba Modeling (DPMM) backbone with complementary Space-Time Sequence Mamba Block (SSMB) and Hilbert Sequence Mamba block (HSMB) for efficient temporal modeling, a Frequency Adaptive Fusion block (FAFB) for degradation-specific feature modulation, and a Universal Multi-degradation Contrastive Learning (UMCL) strategy for robust pattern discovery in multi-degradation scenarios. Experimental results demonstrate that the proposed AIM-VR achieves superior performance in terms of both restoration quality (0.82 dB PSNR over the state-of-the-art methods) and computational efficiency across multiple weather scenarios. Code is available at https://github.com/StephenLockhart/AIM-VR.