Deep consensus semantic aware network for partial multi-view incomplete multi-label classification
Abstract Recently, multi-view multi-label classification (MVMLC) has attracted considerable attention, particularly in the computer vision field. However, due to overlooking incomplete views and missing labels caused by data collection limitations and unreliable annotations, many existing methods ha...
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Springer
2025-05-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00056-9 |
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| author | Bo Shao Yang Xu |
| author_facet | Bo Shao Yang Xu |
| author_sort | Bo Shao |
| collection | DOAJ |
| description | Abstract Recently, multi-view multi-label classification (MVMLC) has attracted considerable attention, particularly in the computer vision field. However, due to overlooking incomplete views and missing labels caused by data collection limitations and unreliable annotations, many existing methods have difficulty in extracting consensus semantic information from diverse data sources. To overcome this challenge, we propose a deep consensus semantic aware network (CSA), which can effectively tackle the challenge of consensus extraction. The proposed CSA incorporates an enhanced contrastive learning (ECL) module, which contains cross-view and cross-fusion contrastive learning. The former facilitates the encoder in capturing and aligning consensus semantic information across multiple views, while the latter maximizes the semantic information of view-specific and view-common representations by decreasing potential interference introduced during the fusion process. In addition, CSA integrates a multi-view cycle generative adversarial (MCGA) network, where the generator reconstructs the original data based on the view-common representation, and the discriminator conducts cycle adversarial training to distinguish between original and generated data, which enhances the extraction of consensus information by forcing the generator to generate actual data. Experiments across five datasets highlight the superiority of CSA. On the Pascal07 dataset, CSA outperforms RANK, the second-best method, with a 0.8% increase in Average Precision (AP) and a 0.8% improvement in Area Under the ROC Curve (AUC). Similarly, on the MIRFLICKR dataset, CSA boosts AP by 1.6% and AUC by 0.5% over RANK. |
| format | Article |
| id | doaj-art-0ff9a341067f4f5188c3120ead8da561 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-0ff9a341067f4f5188c3120ead8da5612025-08-20T03:42:03ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-05-0137411910.1007/s44443-025-00056-9Deep consensus semantic aware network for partial multi-view incomplete multi-label classificationBo Shao0Yang Xu1College of Big Data and Information Engineering, Guizhou UniversityCollege of Big Data and Information Engineering, Guizhou UniversityAbstract Recently, multi-view multi-label classification (MVMLC) has attracted considerable attention, particularly in the computer vision field. However, due to overlooking incomplete views and missing labels caused by data collection limitations and unreliable annotations, many existing methods have difficulty in extracting consensus semantic information from diverse data sources. To overcome this challenge, we propose a deep consensus semantic aware network (CSA), which can effectively tackle the challenge of consensus extraction. The proposed CSA incorporates an enhanced contrastive learning (ECL) module, which contains cross-view and cross-fusion contrastive learning. The former facilitates the encoder in capturing and aligning consensus semantic information across multiple views, while the latter maximizes the semantic information of view-specific and view-common representations by decreasing potential interference introduced during the fusion process. In addition, CSA integrates a multi-view cycle generative adversarial (MCGA) network, where the generator reconstructs the original data based on the view-common representation, and the discriminator conducts cycle adversarial training to distinguish between original and generated data, which enhances the extraction of consensus information by forcing the generator to generate actual data. Experiments across five datasets highlight the superiority of CSA. On the Pascal07 dataset, CSA outperforms RANK, the second-best method, with a 0.8% increase in Average Precision (AP) and a 0.8% improvement in Area Under the ROC Curve (AUC). Similarly, on the MIRFLICKR dataset, CSA boosts AP by 1.6% and AUC by 0.5% over RANK.https://doi.org/10.1007/s44443-025-00056-9Multi-view multi-label classificationConsensus extractionContrastive learningGenerative adversarial networks |
| spellingShingle | Bo Shao Yang Xu Deep consensus semantic aware network for partial multi-view incomplete multi-label classification Journal of King Saud University: Computer and Information Sciences Multi-view multi-label classification Consensus extraction Contrastive learning Generative adversarial networks |
| title | Deep consensus semantic aware network for partial multi-view incomplete multi-label classification |
| title_full | Deep consensus semantic aware network for partial multi-view incomplete multi-label classification |
| title_fullStr | Deep consensus semantic aware network for partial multi-view incomplete multi-label classification |
| title_full_unstemmed | Deep consensus semantic aware network for partial multi-view incomplete multi-label classification |
| title_short | Deep consensus semantic aware network for partial multi-view incomplete multi-label classification |
| title_sort | deep consensus semantic aware network for partial multi view incomplete multi label classification |
| topic | Multi-view multi-label classification Consensus extraction Contrastive learning Generative adversarial networks |
| url | https://doi.org/10.1007/s44443-025-00056-9 |
| work_keys_str_mv | AT boshao deepconsensussemanticawarenetworkforpartialmultiviewincompletemultilabelclassification AT yangxu deepconsensussemanticawarenetworkforpartialmultiviewincompletemultilabelclassification |