14.5.10.8.4 Squeeze-and-Excite Networks

Wang, L.[Li], Peng, J.T.[Jiang-Tao], Sun, W.W.[Wei-Wei],
Spatial-Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Fang, L.Y.[Le-Yuan], Liu, G.Y.[Guang-Yun], Li, S.T.[Shu-Tao], Ghamisi, P.[Pedram], Benediktsson, J.A.[Jón Atli],
Hyperspectral Image Classification with Squeeze Multibias Network,
GeoRS(57), No. 3, March 2019, pp. 1291-1301.
IEEE DOI 1903
convolutional neural nets, hyperspectral imaging, image classification, learning (artificial intelligence), squeeze multibias network (SMBN) BibRef

He, X.[Xin], Chen, Y.S.[Yu-Shi], Ghamisi, P.[Pedram],
Dual Graph Convolutional Network for Hyperspectral Image Classification With Limited Training Samples,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI 2112
Feature extraction, Training, Kernel, Hyperspectral imaging, Data mining, Task analysis, Shape, Classification, label distribution learning BibRef

Xie, J., He, N., Fang, L., Ghamisi, P.,
Multiscale Densely-Connected Fusion Networks for Hyperspectral Images Classification,
CirSysVideo(31), No. 1, January 2021, pp. 246-259.
IEEE DOI 2101
Feature extraction, Training, Testing, Kernel, Hyperspectral imaging, Task analysis, Fuses, Hyperspectral images (HSIs) classification, densebolck BibRef

Roy, A.G.[Abhijit Guha], Navab, N.[Nassir], Wachinger, C.[Christian],
Recalibrating Fully Convolutional Networks With Spatial and Channel 'Squeeze and Excitation' Blocks,
MedImg(38), No. 2, February 2019, pp. 540-549.
IEEE DOI 1902
Image segmentation, Biomedical imaging, Decoding, Task analysis, Encoding, Retina, squeeze and excitation BibRef

Wang, H.R.[Hao-Ran], Ji, Z.[Zhong], Lin, Z.G.[Zhi-Gang], Pang, Y.W.[Yan-Wei], Li, X.L.[Xue-Long],
Stacked squeeze-and-excitation recurrent residual network for visual-semantic matching,
PR(105), 2020, pp. 107359.
Elsevier DOI 2006
Vision and language, Cross-modal retrieval, Visual-Semantic embedding BibRef

Roy, S.K., Chatterjee, S., Bhattacharyya, S., Chaudhuri, B.B., Platoš, J.,
Lightweight Spectral-Spatial Squeeze-and-Excitation Residual Bag-of-Features Learning for Hyperspectral Classification,
GeoRS(58), No. 8, August 2020, pp. 5277-5290.
IEEE DOI 2007
Feature extraction, Hyperspectral imaging, Principal component analysis, Data mining, Training, residual network (ResNet) BibRef

Li, X.[Xian], Ding, M.L.[Ming-Li], Pižurica, A.[Aleksandra],
Deep Feature Fusion via Two-Stream Convolutional Neural Network for Hyperspectral Image Classification,
GeoRS(58), No. 4, April 2020, pp. 2615-2629.
IEEE DOI 2004
BibRef
Earlier:
Group Convolutional Neural Networks for Hyperspectral Image Classification,
ICIP19(639-643)
IEEE DOI 1910
Feature extraction, Training, Streaming media, Machine learning, Hyperspectral imaging, Convolutional neural networks, squeeze-and-excitation (SE). Group convolutional neural networks, multi-scale spectral feature extraction. BibRef

Roy, S.K.[Swalpa Kumar], Dubey, S.R.[Shiv Ram], Chatterjee, S.[Subhrasankar], Chaudhuri, B.B.[Bidyut Baran],
FuSENet: Fused Squeeze-and-Excitation Network for Spectral-Spatial Hyperspectral Image Classification,
IET-IPR(14), No. 8, 19 June 2020, pp. 1653-1661.
DOI Link 2005
BibRef

Wei, S.J.[Shun-Jun], Qu, Q.Z.[Qi-Zhe], Su, H.[Hao], Shi, J.[Jun], Zeng, X.F.[Xiang-Feng], Hao, X.J.[Xiao-Jun],
Intra-pulse modulation radar signal recognition based on Squeeze-and-Excitation networks,
SIViP(14), No. 6, September 2020, pp. 1133-1141.
Springer DOI 2008
BibRef

Tan, H.L.[Han-Lin], Xiao, H.X.[Hua-Xin], Lai, S.M.[Shi-Ming], Liu, Y.[Yu], Zhang, M.J.[Mao-Jun],
Denoising real bursts with squeeze-and-excitation residual network,
IET-IPR(14), No. 13, November 2020, pp. 3095-3104.
DOI Link 2012
BibRef

Hu, J.[Jie], Shen, L.[Li], Albanie, S.[Samuel], Sun, G.[Gang], Wu, E.[Enhua],
Squeeze-and-Excitation Networks,
PAMI(42), No. 8, August 2020, pp. 2011-2023.
IEEE DOI 2007
Computational modeling, Convolution, Task analysis, Correlation, Optimization, convolutional neural networks BibRef

Jin, X.[Xin], Xie, Y.P.[Yan-Ping], Wei, X.S.[Xiu-Shen], Zhao, B.R.[Bo-Rui], Chen, Z.M.[Zhao-Min], Tan, X.Y.[Xiao-Yang],
Delving deep into spatial pooling for squeeze-and-excitation networks,
PR(121), 2022, pp. 108159.
Elsevier DOI 2109
Convolutional neural networks, Squeeze-and-excitation, Spatial pooling, Base model BibRef

Kwon, H.[Heeseung], Kim, M.[Manjin], Kwak, S.[Suha], Cho, M.[Minsu],
Motionsqueeze: Neural Motion Feature Learning for Video Understanding,
ECCV20(XVI: 345-362).
Springer DOI 2010
BibRef

Song, Z., Yuan, Z., Liu, T.,
Residual Squeeze-and-Excitation Network for Battery Cell Surface Inspection,
MVA19(1-5)
DOI Link 1911
feature extraction, inspection, learning (artificial intelligence), object detection, Anomaly detection BibRef

Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.,
GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond,
NeruArch19(1971-1980)
IEEE DOI 2004
convolutional neural nets, image retrieval, object detection, query processing, global context network, network archietcture BibRef

Zhong, X.[Xian], Gong, O.[Oubo], Huang, W.X.[Wen-Xin], Li, L.[Lin], Xia, H.X.[Hong-Xia],
Squeeze-and-Excitation Wide Residual Networks in Image Classification,
ICIP19(395-399)
IEEE DOI 1910
wide residual networks, global pooling, channel, squeeze-and-excitation block, CIFAR BibRef

Hu, J.[Jie], Shen, L.[Li], Sun, G.[Gang],
Squeeze-and-Excitation Networks,
CVPR18(7132-7141)
IEEE DOI 1812
Computational modeling, Convolution, Task analysis, Convolutional codes, Adaptation models, Stacking BibRef

Liu, J., Du, A., Wang, C., Zheng, H., Wang, N., Zheng, B.,
Teaching Squeeze-and-Excitation PyramidNet for Imbalanced Image Classification with GAN-based Curriculum Learning,
ICPR18(2444-2449)
IEEE DOI 1812
Training, Network architecture, Task analysis, Measurement BibRef

Gholami, A., Kwon, K., Wu, B., Tai, Z., Yue, X., Jin, P., Zhao, S., Keutzer, K.,
SqueezeNext: Hardware-Aware Neural Network Design,
EfficientDeep18(1719-171909)
IEEE DOI 1812
Neural networks, Hardware, Semiconductor process modeling, Embedded systems, Power demand, Computational modeling BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Training Issues for Convolutional Neural Networks .