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P_VggNet: A convolutional neural network (CNN) with pixel-based attention map
Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU detection only. Thus, for the use of attention maps...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6291143/ https://www.ncbi.nlm.nih.gov/pubmed/30540804 http://dx.doi.org/10.1371/journal.pone.0208497 |
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author | Liu, Kunhua Zhong, Peisi Zheng, Yi Yang, Kaige Liu, Mei |
author_facet | Liu, Kunhua Zhong, Peisi Zheng, Yi Yang, Kaige Liu, Mei |
author_sort | Liu, Kunhua |
collection | PubMed |
description | Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU detection only. Thus, for the use of attention maps in every image type, this paper proposed a new convolutional neural network (CNN) structure, P_VggNet, comprising the following parts: P_Net and VggNet with 16 layers (VggNet-16). The generation approach of P_Net was designed, and the P_VggNet structure was proposed. To prove the efficiency of P_VggNet, we designed two experiments, which indicated that P_VggNet could more efficiently extract image features than VggNet-16. |
format | Online Article Text |
id | pubmed-6291143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-62911432018-12-28 P_VggNet: A convolutional neural network (CNN) with pixel-based attention map Liu, Kunhua Zhong, Peisi Zheng, Yi Yang, Kaige Liu, Mei PLoS One Research Article Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU detection only. Thus, for the use of attention maps in every image type, this paper proposed a new convolutional neural network (CNN) structure, P_VggNet, comprising the following parts: P_Net and VggNet with 16 layers (VggNet-16). The generation approach of P_Net was designed, and the P_VggNet structure was proposed. To prove the efficiency of P_VggNet, we designed two experiments, which indicated that P_VggNet could more efficiently extract image features than VggNet-16. Public Library of Science 2018-12-12 /pmc/articles/PMC6291143/ /pubmed/30540804 http://dx.doi.org/10.1371/journal.pone.0208497 Text en © 2018 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liu, Kunhua Zhong, Peisi Zheng, Yi Yang, Kaige Liu, Mei P_VggNet: A convolutional neural network (CNN) with pixel-based attention map |
title | P_VggNet: A convolutional neural network (CNN) with pixel-based attention map |
title_full | P_VggNet: A convolutional neural network (CNN) with pixel-based attention map |
title_fullStr | P_VggNet: A convolutional neural network (CNN) with pixel-based attention map |
title_full_unstemmed | P_VggNet: A convolutional neural network (CNN) with pixel-based attention map |
title_short | P_VggNet: A convolutional neural network (CNN) with pixel-based attention map |
title_sort | p_vggnet: a convolutional neural network (cnn) with pixel-based attention map |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6291143/ https://www.ncbi.nlm.nih.gov/pubmed/30540804 http://dx.doi.org/10.1371/journal.pone.0208497 |
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