Image clustering is a fundamental task in unsupervised visual learning. While recent self-supervised methods have explored various pretext tasks to generate supervision signals for clustering, they typically depend exclusively on raw images, resulting in insufficient supervision signals that are inherently constrained by limited visual semantics. In this paper, we propose a novel Semantic-Augmented image Clustering (SAC) method, which transcends the inherent limitations of purely visual representations through the integration of external knowledge. Specifically, SAC utilizes Vision-Language pre-trained Models (VLMs) to flexibly generate textual descriptions for each image, providing external semantic cues to supplement the visual information. By integrating both visual and textual information, SAC achieves image clustering through a multi-modal learning framework. To mitigate the negative impact of inaccurate textual information, SAC designs an uncertainty-driven adaptive weighting mechanism that explores both intra-modal and inter-modal neighborhood structures, and incorporates the adaptive weights into intra-modal and inter-modal contrastive learning, which improves the robustness against noisy image-text correspondences. Experiments on several popular datasets demonstrate the superiority of SAC compared to state-of-the-art methods.

Recently, anchor-based incomplete multi-view clustering (IMVC) has been widely adopted for fast clustering, but most existing approaches still encounter some issues: (1) They generally rely on the observed samples to construct anchor graphs, ignoring the potentially useful information of missing instances. (2) Most methods attempt to learn a consensus anchor graph, failing to fully excavate the complementary information and high-order correlations across views. (3) They generally apply post-processing on learned anchor graph to seek latent embeddings, making them not globally-optimal. To address these issues, this paper proposes a novel fast IMVC approach with Adaptive Similarity Completion and Reconstruction (ASCR), which unifies anchor learning, anchor-sample similarity construction and completion, and latent multi-view embedding learning in a joint framework. Specifically, ASCR learns an anchor-sample similarity graph for each view, and the missing values are fulfilled to mitigate the adverse effects. To explore the consistent and complementary information across views, ASCR simultaneously seeks the view-specific anchor embeddings and sample embeddings in a latent subspace by similarity reconstruction, which not only preserves the semantic information into latent embeddings but also enhances the low-rank property of similarity graphs, achieving a reliable graph completion process. Furthermore, the high-order cross-view correlations are explored with tensor-based regularization. Extensive experimental results demonstrate the superiority and efficiency of ASCR compared with SOTA approaches.

Multi-view clustering has attracted increasing attention in recent years. However, most existing multi-view clustering approaches are performed in a purely unsupervised manner, while ignoring the valuable weak supervision information that can be obtained (e.g., active query) in many real applications. This paper considers the weak pairwise constraints among samples to enhance the clustering performance, and proposes a Semi-supervised Multi-view Clustering method with Active Constraints, SMCAC for short. SMCAC consists of two stages, clustering (C-stage) and active query (A-stage). In the C-stage, we design a tensor based multi-view graph learning model equipped with sample pairwise constraints regularization to facilitate the discriminative graph learning and fusion. An effective optimization algorithm based on alternating direction minimization is devised to solve the clustering model. In the A-stage, the most uncertain or difficult sample pairs are actively selected to query the constraints, based on the divergence of multi-view similarities learned in the C-stage. The two processes alternate iteratively until the maximum number of queries is reached. Extensive experiments on several popular datasets well validate the effectiveness of the proposed method.

Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing methods still encounter some limitations. (1) Most of them explore the correlations among different affinity matrices, making them unscalable to large-scale data. (2) Although some methods address it by introducing bipartite graphs, they may result in sub-optimal solutions caused by an unstable anchor selection process. (3) They generally ignore the negative impact of latent semantic-unrelated information in each view. To tackle these issues, we propose a new approach termed fast Disentangled Slim Tensor Learning (DSTL) for multi-view clustering. Instead of focusing on the multi-view graph structures, DSTL directly explores the high-order correlations among multi-view latent semantic representations based on matrix factorization. To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view. Subsequently, two slim tensors are constructed with tensor-based regularization. To further enhance the quality of feature disentanglement, the semantic-related representations are aligned across views through a consensus alignment indicator. Our proposed model is computationally efficient and can be solved effectively. Extensive experiments demonstrate the superiority and efficiency of DSTL over state-of-the-art approaches.