Results 251 to 260 of about 1,699,128 (261)

Robust Multi-View Prototype Learning

2020 International Conference on Internet of Things and Intelligent Applications (ITIA), 2020
In vision and machine learning, information fusion from multiple sensors can be regarded as multi-view learning paradigm to make use of the pairwise complementary information. Due to disturbed variances by illumination, equipment and environment, the collected data is frequently smeared by noises. Although there have been outlier-against works proposed,
Qing Tian, Shiyu Xia, Meng Cao, Keyang Cheng   +3 more
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Multi-view dynamic texture learning

2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016
Dynamic texture (DT) provides a flexible and suitable tool for representing phenomena over space and time. We focus here on DT learning for multi-view domains, each of which is sufficient to learn the target concept. We make several contributions in this paper. First, we derive new features and then present their use in our description of DT.
Thanh Minh Nguyen, Q. M. Jonathan Wu
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Robust Multi-view Subspace Learning

2017
By virtue of the increasingly large amount of various sensors, information about the same object can be collected from multiple views. These mutually enriched information can help many real-world applications, such as daily activity recognition in which both video cameras and on-body sensors are continuously collecting information.
Sheng Li, Yun Fu
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Biased Incomplete Multi-View Learning

Proceedings of the AAAI Conference on Artificial Intelligence
Considering the ubiquitous phenomenon of missing views in multi-view data, incomplete multi-view learning is a crucial task in many applications. Existing methods usually follow an impute-then-predict strategy for handling this problem. However, they often assume that the view-missing patterns are uniformly random in multi-view data, which does not ...
Haishun Chen, Cai Xu, Ziyu Guan, Wei Zhao, Jinlong Liu   +4 more
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Deep Generative Multi-view Learning

2020
Deep generative networks has attracted proliferating interests recently. In this work, the linear generative multi-view model is extended to nonlinear multi-views model where the deep neural network is leveraged to model complex latent representation underlying the multi-view observation.
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Comprehensive Multi-view Representation Learning

Information Fusion, 2023
Qinghai Zheng, Jihua Zhu, Zhongyu Li, Zhiqiang Tian, Chen Li   +4 more
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Multi-View Representation Learning

2022
G. Muthu Lakshmi, N. Krishnammal
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Efficient federated multi-view learning

Pattern Recognition, 2022
Shudong Huang, Wei Shi, Zenglin Xu, Ivor W. Tsang, Jiancheng Lv   +4 more
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Learning for Multi-view Tracking

In response to the growing trend towards end-to-end learning, we propose a novel framework advancing towards an end-to-end multi-camera multi-object tracking (MC-MOT) solution that addresses challenges like occlusions, viewpoint variations, and illumination changes.
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Unbalanced Multi-view Deep Learning

Proceedings of the 31st ACM International Conference on Multimedia, 2023
Cai Xu, Zehui Li, Ziyu Guan, Wei Zhao, Xiangyu Song, Yue Wu, Jianxin Li   +6 more
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