Multi-view learning has become a popular research topic in recent years, but
research on the cross-application of classic multi-label classification and
multi-view learning is still in its early stages. In this paper, we focus on
the complex yet highly realistic task of incomplete multi-view weak multi-label
learning and propose a masked two-channel decoupling framework based on deep
neural networks to solve this problem. The core innovation of our method lies
in decoupling the single-channel view-level representation, which is common in
deep multi-view learning methods, into a shared representation and a
view-proprietary representation. We also design a cross-channel contrastive
loss to enhance the semantic property of the two channels. Additionally, we
exploit supervised information to design a label-guided graph regularization
loss, helping the extracted embedding features preserve the geometric structure
among samples. Inspired by the success of masking mechanisms in image and text
analysis, we develop a random fragment masking strategy for vector features to
improve the learning ability of encoders. Finally, it is important to emphasize
that our model is fully adaptable to arbitrary view and label absences while
also performing well on the ideal full data. We have conducted sufficient and
convincing experiments to confirm the effectiveness and advancement of our
model.

提出了一种基于深度神经网络的掩码两通道解耦框架，用于解决复杂而高度逼真的不完全多视角弱多标签学习任务，核心创新在于将深度多视学习方法中的单通道视图级表示解耦为共享表示和视图专有表示，并设计了交叉通道对比损失以增强两个通道的语义属性。此外，利用监督信息设计了标签引导的图正则化损失，帮助提取的嵌入特征保持样本间的几何结构，并开发了随机片段掩码策略来改善编码器的学习能力。值得强调的是，我们的模型完全适应任意视图和标签缺失，并在理想完整数据上表现良好，在充分充足的实验证明了我们模型的有效性和先进性。