Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification

Wulin Xie, Lian Zhao, Jiang Long, Xiaohuan Lu, Bingyan Nie; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1914-1923

Multi-view multi-label classification (MvMLC) has recently garnered significant research attention due to its wide range of real-world applications. However incompleteness in views and labels is a common challenge often resulting from data collection oversights and uncertainties in manual annotation. Furthermore the task of learning robust multi-view representations that are both view-consistent and view-specific from diverse views still a challenge problem in MvMLC. To address these issues we propose a novel framework for incomplete multi-view multi-label classification (iMvMLC). Our method factorizes multi-view representations into two independent sets of factors: view-consistent and view-specific and we correspondingly design a graph disentangling loss to fully reduce redundancy between these representations. Additionally our framework innovatively decomposes consistent representation learning into three key sub-objectives: (i) how to extract view-shared information across different views (ii) how to eliminate intra-view redundancy in consistent representations and (iii) how to preserve task-relevant information. To this end we design a robust task-relevant consistency learning module that collaboratively learns high-quality consistent representations leveraging a masked cross-view prediction (MCP) strategy and information theory. Notably all modules in our framework are developed to function effectively under conditions of incomplete views and labels making our method adaptable to various multi-view and multi-label datasets. Extensive experiments on five datasets demonstrate that our method outperforms other leading approaches.