  As a cross-topic of multi-view learning and multi-label classification,
multi-view multi-label classification has gradually gained traction in recent
years. The application of multi-view contrastive learning has further
facilitated this process, however, the existing multi-view contrastive learning
methods crudely separate the so-called negative pair, which largely results in
the separation of samples belonging to the same category or similar ones.
Besides, plenty of multi-view multi-label learning methods ignore the possible
absence of views and labels. To address these issues, in this paper, we propose
an incomplete multi-view partial multi-label classification network named RANK.
In this network, a label-driven multi-view contrastive learning strategy is
proposed to leverage supervised information to preserve the structure within
view and perform consistent alignment across views. Furthermore, we break
through the view-level weights inherent in existing methods and propose a
quality-aware sub-network to dynamically assign quality scores to each view of
each sample. The label correlation information is fully utilized in the final
multi-label cross-entropy classification loss, effectively improving the
discriminative power. Last but not least, our model is not only able to handle
complete multi-view multi-label datasets, but also works on datasets with
missing instances and labels. Extensive experiments confirm that our RANK
outperforms existing state-of-the-art methods.
