View-Category Interactive Sharing Transformer for Incomplete Multi-View Multi-Label Learning

Shilong Ou, Zhe Xue, Yawen Li, Meiyu Liang, Yuanqiang Cai, Junjiang Wu

Abstract: As a problem often encountered in real-world scenarios, multi-view multi-label learning has attracted considerable research attention. However, due to oversights in data col-lection and uncertainties in manual annotation, real-world data often suffer from incompleteness. Regrettably, most existing multi-view multi-label learning methods sidestep missing views and labels. Furthermore, they often neglect the potential of harnessing complementary information be-tween views and labels, thus constraining their classification capabilities. To address these challenges, we propose a view-category interactive sharing transformer tailored for incomplete multi-view multi-label learning. Within this net-work, we incorporate a two-layer transformer module to characterize the interplay between views and labels. Additionally, to address view incompleteness, a KNN-style missing view generation module is employed. Finally, we in-troduce a view-category consistency guided embedding en-hancement module to align different views and improve the discriminating power of the embeddings. Collectively, these modules synergistically integrate to classify the incomplete multi-view multi-label data effectively. Extensive experi-ments substantiate that our approach outperforms the ex-isting state-of-the-art methods.