Title: HCDA: Hierarchical Contrastive Learning with Dynamic Attention Fusion
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Multi-view clustering，Hierarchical contrastive learning，Dynamic attention fusion

Abstract: Multi-view clustering integrates complementary features and semantics across modalities to provide comprehensive data representations. However, existing methods struggle with semantic fragmentation and inefficient view integration when processing complex multi-view data. To address these challenges, this paper proposes a Hierarchical Contrastive learning and Dynamic Attention (HCDA) fusion framework.
HCDA achieves fine-grained semantic decoupling by constructing multi-level semantic spaces (independent semantic space, cross-view fused semantic space, and global semantic space), and designs a dual-path attention mechanism to dynamically allocate view weights based on global semantics, suppressing interference from low-quality views while enhancing the dominant role of high-contribution views.
Additionally, HCDA jointly optimizes contrastive losses between cross-view fused features and independent features to balance view commonality and specificity, enhancing both discriminability and consistency.
Experimental results demonstrate HCDA's superiority over state-of-the-art methods on benchmark datasets. Ablation studies confirm the efficacy of hierarchical semantic modeling and dynamic weighting, while parameter analysis highlights robustness to hyperparameter variations.