Results 1 to 10 of about 1,697,121 (115)
Transductive Multi-View Zero-Shot Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 (c) 2012. The copyright of this document resides with its authors.
Fu, Y, Gong, S, Hospedales, TM, Xiang, T
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Multi-View Learning for Material Classification. [PDF]
J Imaging, 2022 Material classification is similar to texture classification and consists in predicting the material class of a surface in a color image, such as wood, metal, water, wool, or ceramic. It is very challenging because of the intra-class variability.
Sumon BU, Muselet D, Xu S, Trémeau A.
europepmc +5 more sources
Deep Multi-view Learning to Rank [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2019 We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has
Cao, Guanqun +4 more
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PAC-Bayes Analysis of Multi-view Learning [PDF]
Information Fusion, 2016 This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers. These bounds adopt data dependent Gaussian priors which emphasize classifiers with high view agreements. The center of the prior for the first
Mao, Liang +2 more
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Deep Partial Multi-View Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets), which ...
Zhang, Changqing +5 more
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Few-Shot Partial Multi-View Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity.
Yuan Zhou +4 more
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Multi-View Intact Space Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 It is practical to assume that an individual view is unlikely to be sufficient for effective multi-view learning. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to ...
Xu, Chang, Tao, Dacheng, Xu, Chao
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Multi-view dreaming: multi-view world model with contrastive learning
Advanced Robotics, 2023 7 pages, 8 ...
Akira Kinose +3 more
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Tensorized Multi-view Subspace Representation Learning [PDF]
International Journal of Computer Vision, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Changqing +5 more
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Semantically consistent multi-view representation learning
Knowledge-Based Systems, 2023 19 pages ...
Yiyang Zhou +3 more
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