No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion-Reference-Cited by-同舟云学术

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No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion

Published:2013-12 Issue: Volume: Page:
ISSN:
Container-title:2013 IEEE International Conference on Computer Vision
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Author:

Yan Yan,Ricci Elisa,Subramanian Ramanathan,Lanz Oswald,Sebe Nicu

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/6750807/6751100/06751256.pdf?arnumber=6751256

Cited by 86 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Infants' Developing Environment: Integration of Computer Vision and Human Annotation to Quantify where Infants Go, what They Touch, and what They See;2024 IEEE International Conference on Development and Learning (ICDL);2024-05-20

2. Deep Learning for Head Pose Estimation: A Survey;SN Computer Science;2023-04-26

3. DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image;2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2022-06

4. Sub-Optimal Head Position Classification Using OpenVINO and SVM;SoutheastCon 2022;2022-03-26

5. Hierarchical Pose Classification for Infant Action Analysis and Mental Development Assessment;ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2021-06-06

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