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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...

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Detalles Bibliográficos
Autores principales: Liu, Kunhua, Zhong, Peisi, Zheng, Yi, Yang, Kaige, Liu, Mei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
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.
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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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