Results 241 to 250 of about 5,726,851 (263)

Global-affinity constraint with distillation-enhanced network for dual incomplete multi-view multi-label classification

Signal Processing
Yinghao Ye, Xiaohuan Lu
exaly   +3 more sources

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning

Neural Networks
Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the assumption of data completeness, making them useless in practical applications with partially missing ...
Jie Wen, Jie Wen, Mu Li
exaly   +3 more sources

Tucker decomposition guided shared latent label learning for multi-view incomplete multi-label classification

Information Fusion
Tingquan Deng, Yiying Chen, Qingwei Jia
exaly   +3 more sources

Learning Compact Semantic Information and Reliable Pseudo-Labels for Incomplete Multi-View Multi-Label Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view data encompasses various data types, including multi-feature, multi-sequence, and multi-modal data. Multi-view multi-label classification aims to leverage the rich semantic information contained in multiple views to achieve enhanced multi-label classification performance.
Yadong Liu, Chengliang Liu, Jie Wen, Li Shen, Bob Zhang, Yong Xu   +5 more
openaire   +2 more sources

Dual-Space Structure Preservation with Non-Homogeneous Fusion for Incomplete Multi-view Multi-label Classification

Communications in Computer and Information Science
Xiaohuan Lu
exaly   +2 more sources

Quality-aware and Soft Consistency Driven Representation Fusion for Incomplete Multi-view Multi-label Classification

Proceedings of the AAAI Conference on Artificial Intelligence
Multi-view multi-label classification aims to utilize the rich information contained in multiple views for accurate classification. However, in real-world applications, its performance is often severely constrained by the concurrent missingness of both views and labels.
Yadong Liu, Waikeung Wong, Yulong Chen, Jie Wen 0001   +3 more
openaire   +1 more source

DMVMLC-VT: Deep incomplete multi-view multi-label image classification with view translation and pseudo-label enhancement

Visual Computer
Changming Zhu
exaly   +3 more sources

Learning Reliable Representations for Incomplete Multi-View Partial Multi-Label Classification.

CoRR, 2023
Chengliang Liu 0003, Jie Wen 0001, Yong Xu 0001, Liqiang Nie, Min Zhang 0005   +4 more
openaire   +1 more source

Multi-faceted Complementary Learning for Incomplete Multi-view Multi-label Classification

Proceedings of the 33rd ACM International Conference on Multimedia
Xinyu Xiao, Peixi Peng, Qiang Wang 0022, Chao Xing, Shuhan Qi   +4 more
openaire   +2 more sources

Takagi-Sugeno-Kang Fuzzy System Towards Label-scarce Incomplete Multi-View Data Classification

Information Sciences, 2023
Zhaohong Deng, Qiongdan Lou, Shitong Wang   +2 more
exaly