论文概要
研究领域: CV 作者: Xu Yan, Jun Yin, Shiliang Sun中文摘要
尽管多视图多标签学习已被广泛研究，但对双重缺失场景（视图和标签均不完整）的研究在很大程度上尚未被探索。现有方法主要依赖对比学习或信息瓶颈理论来学习缺失视图条件下的一致表示，但基于损失的对齐而没有明确的结构约束限制了捕获稳定且有区分性的共享语义的能力。为解决这一问题，我们引入了一种更结构化的机制来进行一致表示学习：我们通过多视图共享码本和跨视图重建来学习离散一致表示，这在有限的共享码本嵌入内自然地校准不同视图并减少特征冗余。在决策层面，我们设计了一种权重估计方法，评估每个视图保持标签相关结构的能力，相应地分配权重以提高融合预测的质量。此外，我们引入了融合教师自蒸馏框架，其中融合预测指导视图特定分类器的训练，并将全局知识反馈到单视图分支中，从而增强模型在缺失标签条件下的泛化能力。所提方法的有效性通过在五个基准数据集上与先进方法进行的广泛对比实验得到充分证明。代码可在 https://github.com/xuy11/SCSD 获取。原文摘要
Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contrastive learning or information bottleneck theory to learn consistent representations under missing-view conditions, but loss-based alignment without explicit structural constraints limits the ability to capture stable and discriminative shared semantics. To address this issue, we introduce a more structured mechanism for consistent representation learning: we learn discrete consistent representations through a multi-view shared codebook and cross-view reconstruction, which naturally align different views within the limited shared codebook embeddings and reduce feature redundancy. At the decision level, we design a weight estimation method that evaluates the ability of each view to preserve label correlation structures, assigning weights accordingly to enhance the quality of the fused prediction. In addition, we introduce a fused-teacher self-distillation framework, where the fused prediction guides the training of view-specific classifiers and feeds the global knowledge back into the single-view branches, thereby enhancing the generalization ability of the model under missing-label conditions. The effectiveness of our proposed method is thoroughly demonstrated through extensive comparative experiments with advanced methods on five benchmark datasets. Code is available at https://github.com/xuy11/SCSD.--- *自动采集于 2026-04-07*
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