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作者:J Hu2018被引用次数:7765 — Squeeze-and-Excitation Networks. Abstract: Convolutional neural networks are built upon the convolution operation, which extracts informative features by ...
Date Added to IEEE Xplore: 17 December 2018
Date of Conference: 18-23 June 2018
DOI: 10.1109/CVPR.2018.00745
INSPEC Accession Number: 18326366
作者:J Hu2017被引用次数:7765 — Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which ... Cite as: arXiv:1709.01507 [cs.
作者:J Hu2020被引用次数:7765 — The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct ...
Squeeze-and-Excitation Networks. J. Hu, L. Shen, G. Sun. IEEE Conference on Computer Vision and Pattern Recognition, 2018.
作者:J Hu被引用次数:7765 — The feature maps U are then reweighted to generate the output of the SE block which can then be fed directly into subsequent layers. An SE network can be ...
Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information ...
Request PDF | Squeeze-and-Excitation Networks | Convolutional neural networks are built upon ... Find, read and cite all the research you need on ResearchGate.
Jie Hu, Li Shen, Gang Sun: Squeeze-and-Excitation Networks. CVPR 2018: 7132-7141. a service of Schloss Dagstuhl - Leibniz Center for Informatics.
作者:Z Hu2021 — formation from convolutional neural networks (CNNs) but ignore the ... (MFEA) and a feature squeeze-and-excitation hierarchical ...
2021年3月9日 — Squeeze-and-Excitation (SE) Networks won the last ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) classification competition and ...

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