Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908278790815744 |
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| author | Zhao, Lian Wen, Jie Lu, Xiaohuan Wong, Wai Keung Long, Jiang Xie, Wulin |
| author_facet | Zhao, Lian Wen, Jie Lu, Xiaohuan Wong, Wai Keung Long, Jiang Xie, Wulin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification Zhao, Lian Wen, Jie Lu, Xiaohuan Wong, Wai Keung Long, Jiang Xie, Wulin Computer Vision and Pattern Recognition 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. |
| title | Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07931 |