Ensembled Transfer Learning Based Multichannel Attention Networks for Human Activity Recognition in Still Images

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Vydáno v:IEEE Access Ročník 10; s. 47051 - 47062
Hlavní autoři: Koki Hirooka, Md. Al Mehedi Hasan, Jungpil Shin, Azmain Yakin Srizon
Médium: Journal Article
Jazyk:japonština
Vydáno: Institute of Electrical and Electronics Engineers (IEEE) 01.01.2022
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ISSN:2169-3536
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Author Md. Al Mehedi Hasan
Azmain Yakin Srizon
Jungpil Shin
Koki Hirooka
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SubjectTerms Action Recognition
Artificial intelligence
Channel (broadcasting)
Computer network
Computer science
Computer Vision and Pattern Recognition
Convolutional neural network
Deep learning
Deep Learning in Computer Vision and Image Recognition
Electrical engineering. Electronics. Nuclear engineering
ensembling
Feature (linguistics)
Feature extraction
Foreground Segmentation
FOS: Languages and literature
FOS: Philosophy, ethics and religion
Human Action Recognition and Pose Estimation
Human Activity Analysis
Human activity recognition
InceptionResnetV2
InceptionV3
Linguistics
Machine learning
Margin (machine learning)
Motion Detection
multi-channel attention module
Multiple Object Tracking
Pattern recognition (psychology)
Philosophy
Physical Sciences
TK1-9971
Transfer of learning
Visual Object Tracking and Person Re-identification
Xception
Title Ensembled Transfer Learning Based Multichannel Attention Networks for Human Activity Recognition in Still Images
URI https://cir.nii.ac.jp/crid/1873679867270376448
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