Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification
<|reference_start|>Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification: In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view features impairs the comprehensive understanding of samples, omitting crucial details essential for classification. To address this issue, we present a task-augmented cross-view imputation network (TACVI-Net) for the purpose of handling partial multi-view incomplete multi-label classification. Specifically, we employ a two-stage network to derive highly task-relevant features to recover the missing views. In the first stage, we leverage the information bottleneck theory to obtain a discriminative representation of each view by extracting task-relevant information through a view-specific encoder-classifier architecture. In the second stage, an autoencoder based multi-view reconstruction network is utilized to extract high-level semantic representation of the augmented features and recover the missing data, thereby aiding the final classification task. Extensive experiments on five datasets demonstrate that our TACVI-Net outperforms other state-of-the-art methods.<|reference_end|>
@article{lu2024task-augmented,
    title={Task-Augmented Cross-View Imputation Network for Partial Multi-View
  Incomplete Multi-Label Classification},
    author={Xiaohuan Lu, Lian Zhao, Wai Keung Wong, Jie Wen, Jiang Long, Wulin Xie},
    journal={arXiv preprint arXiv:2409.07931},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07931},
    primaryClass={cs.CV}
}