Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911854717042688 |
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| author | Liu, Chengliang Wen, Jie Liu, Yabo Huang, Chao Wu, Zhihao Luo, Xiaoling Xu, Yong |
| author_facet | Liu, Chengliang Wen, Jie Liu, Yabo Huang, Chao Wu, Zhihao Luo, Xiaoling Xu, Yong |
| contents | Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages. In this paper, we focus on the complex yet highly realistic task of incomplete multi-view weak multi-label learning and propose a masked two-channel decoupling framework based on deep neural networks to solve this problem. The core innovation of our method lies in decoupling the single-channel view-level representation, which is common in deep multi-view learning methods, into a shared representation and a view-proprietary representation. We also design a cross-channel contrastive loss to enhance the semantic property of the two channels. Additionally, we exploit supervised information to design a label-guided graph regularization loss, helping the extracted embedding features preserve the geometric structure among samples. Inspired by the success of masking mechanisms in image and text analysis, we develop a random fragment masking strategy for vector features to improve the learning ability of encoders. Finally, it is important to emphasize that our model is fully adaptable to arbitrary view and label absences while also performing well on the ideal full data. We have conducted sufficient and convincing experiments to confirm the effectiveness and advancement of our model. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_17340 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning Liu, Chengliang Wen, Jie Liu, Yabo Huang, Chao Wu, Zhihao Luo, Xiaoling Xu, Yong Computer Vision and Pattern Recognition Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages. In this paper, we focus on the complex yet highly realistic task of incomplete multi-view weak multi-label learning and propose a masked two-channel decoupling framework based on deep neural networks to solve this problem. The core innovation of our method lies in decoupling the single-channel view-level representation, which is common in deep multi-view learning methods, into a shared representation and a view-proprietary representation. We also design a cross-channel contrastive loss to enhance the semantic property of the two channels. Additionally, we exploit supervised information to design a label-guided graph regularization loss, helping the extracted embedding features preserve the geometric structure among samples. Inspired by the success of masking mechanisms in image and text analysis, we develop a random fragment masking strategy for vector features to improve the learning ability of encoders. Finally, it is important to emphasize that our model is fully adaptable to arbitrary view and label absences while also performing well on the ideal full data. We have conducted sufficient and convincing experiments to confirm the effectiveness and advancement of our model. |
| title | Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.17340 |