Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Christopher Reale"'
Publikováno v:
IEEE Access, Vol 3, Pp 1620-1632 (2015)
Several models have been previously suggested for learning correlated representations between source and target modalities. In this paper, we propose a novel coupled autoassociative neural network for learning a target-to-source image representation
Externí odkaz:
https://doaj.org/article/4292d6be46e94fd8be6a4e95f7de5dfc
Publikováno v:
2022 IEEE Aerospace Conference (AERO).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8dee5ef79d0a2c21e50ff6b45987268
https://doi.org/10.1109/aero53065.2022.9843687
https://doi.org/10.1109/aero53065.2022.9843687
Autor:
Nicholas Conlon, Aastha Acharya, Jamison McGinley, Trevor Slack, Camron A. Hirst, Marissa D'Alonzo, Mitchell R. Hebert, Christopher Reale, Eric W. Frew, Rebecca Russell, Nisar R. Ahmed
Publikováno v:
AIAA SCITECH 2022 Forum.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8988638c7b058980b6f3e71936845ccc
https://doi.org/10.2514/6.2022-2496
https://doi.org/10.2514/6.2022-2496
Publikováno v:
ICASSP
Many previous methods have demonstrated the importance of considering semantically relevant objects for carrying out video-based human activity recognition, yet none of the methods have harvested the power of large text corpora to relate the objects
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51ad32a5690cb21e2785c3653ec57d4b
Publikováno v:
IEEE Access, Vol 3, Pp 1620-1632 (2015)
Several models have been previously suggested for learning correlated representations between source and target modalities. In this paper, we propose a novel coupled autoassociative neural network for learning a target-to-source image representation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30e89160096914c8f06b5c0406628ce1
https://ieeexplore.ieee.org/document/7270978/
https://ieeexplore.ieee.org/document/7270978/
Publikováno v:
CVPR Workshops
In this work we present three methods to improve a deep convolutional neural network approach to near-infrared heterogeneous face recognition. We first present a method to distill extra information from a pre-trained visible face network through the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9f7be16901b83cef124c047c9e3dcddd
https://doi.org/10.1109/cvprw.2017.34
https://doi.org/10.1109/cvprw.2017.34
Publikováno v:
FG
In recent years, deep learning has emerged as a dominant methodology in virtually all machine learning problems. While it has been shown to produce state-of-the-art results for a variety of applicatons (including face recognition and heterogeneous fa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::888f08de3439894fed84a7cdaa47ce09
https://doi.org/10.1109/fg.2017.128
https://doi.org/10.1109/fg.2017.128
Publikováno v:
GlobalSIP
In recent years, state-of-the-art face recognition performance has improved by using deep convolutional neural networks. One disadvantage of these methods is their need for very large, labeled training datasets as collecting and labeling them can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::595cce48ae4a80efcbfeb7157c7f17d6
https://doi.org/10.1109/globalsip.2016.7906030
https://doi.org/10.1109/globalsip.2016.7906030
Publikováno v:
CVPR Workshops
Heterogeneous face recognition is the problem of identifying a person from a face image acquired with a nontraditional sensor by matching it to a visible gallery. Most approaches to this problem involve modeling the relationship between corresponding
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::278e4768e75307fe2de3375dd3f0a5f6
https://doi.org/10.1109/cvprw.2016.47
https://doi.org/10.1109/cvprw.2016.47
Publikováno v:
Face Recognition Across the Imaging Spectrum ISBN: 9783319284996
Face Recognition Across the Imaging Spectrum
Face Recognition Across the Imaging Spectrum
Cross-spectral face recognition, which seeks to match a face image acquired in one spectral band (e.g., infrared) to that of a face acquired in another band (e.g., visible), is a relatively new area of research in the biometrics community. Thermal-to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2efbf6f9965efdf7c4654473eb1a8cb
https://doi.org/10.1007/978-3-319-28501-6_4
https://doi.org/10.1007/978-3-319-28501-6_4