Results 11 to 20 of about 5,726,851 (263)

Structure-guided decoupled contrastive framework for partial multi-view incomplete multi-label classification

Journal of King Saud University: Computer and Information Sciences
Recently, multi-view multi-label learning has gained significant attention due to its applicability in various domains. However, due to the limitations of data collection and the subjectivity of manual labeling, multi-view multi-label learning often ...
Linqian Yang, Tao Zhuang, Xing Jin, Tianyang Cao, Xiyin Wu   +4 more
doaj   +4 more sources

DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means ...
Xu, Yong, Luo, Xiaoling, Huang, Chao, Wu, Zhihao, Liu, Chengliang, Wen, Jie   +5 more
core   +5 more sources

Siamese network with squeeze-attention for incomplete multi-view multi-label classification

Complex & Intelligent Systems
Multi-view multi-label classification (MvMLC) has garnered significant interest because of its ability to handle complex datasets. However, the inherent complexity of real-world data often results in incomplete views and missing labels, which limit the ...
Mengqing Wang, Jiarui Chen, Lian Zhao, Yinghao Ye, Xiaohuan Lu   +4 more
doaj   +2 more sources

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

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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 ...
Long, Jiang, Zhao, Lian, Xie, Wulin, Nie, Bingyan, Lu, Xiaohuan   +4 more
core   +5 more sources

Incomplete multi-view partial multi-label classification via deep semantic structure preservation

Complex & Intelligent Systems
Recent advances in multi-view multi-label learning are often hampered by the prevalent challenges of incomplete views and missing labels, common in real-world data due to uncertainties in data collection and manual annotation.
Chaoran Li, Xiyin Wu, Pai Peng, Zhuhong Zhang, Xiaohuan Lu   +4 more
doaj   +2 more sources

Deep consensus semantic aware network for partial multi-view incomplete multi-label classification

Journal of King Saud University: Computer and Information Sciences
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 ...
Bo Shao, Yang Xu
doaj   +2 more sources

Semantic consistency enhancement and contribution-driven network for partial multi-view incomplete multi-label classification

Journal of King Saud University: Computer and Information Sciences
In recent years, multi-view multi-label learning has garnered considerable attention due to its broad application prospects, such as bioinformatics and medical imaging.
Yishan Jiang, Lian Zhao, Zhixian Jiang, Yinghao Ye, Xiaohuan Lu   +4 more
doaj   +3 more sources

Dynamic graph-guided imputation network for partial multi-view incomplete multi-label classification

Journal of King Saud University: Computer and Information Sciences
In practice, multi-view multi-label classification often faces the dual challenge of missing views and labels. Existing methods typically avoid redundant computations by simply masking missing items, which neither recovers missing view information nor ...
Xingang Mao, Yang Xu
doaj   +2 more sources

A Two-Stage Information Extraction Network for Incomplete Multi-View Multi-Label Classification

Proceedings of the AAAI Conference on Artificial Intelligence
Recently, multi-view multi-label classification (MvMLC) has received a significant amount of research interest and many methods have been proposed based on the assumptions of view completion and label completion.
Tan, Xin, Zhao, Ce, Tang, Zhanyan, Liu, Chengliang, Wen, Jie   +4 more
core   +3 more sources

Automatic multi-label subject indexing in a multilingual environment [PDF]

, 2003
This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset.
Andreas Hotho, Lauser, Boris, Boris Lauser, Hotho, Andreas   +3 more
core   +2 more sources