Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

Learning to detect unseen object classes by between-class attribute transfer

Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009

Earlier work this paper cites.

Imagenet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009

Earlier work this paper cites.

Learning multiple layers of features from tiny images

Alex Krizhevsky, Geoffrey Hinton, et al · 2009

Earlier work this paper cites.

Imagenet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012

Earlier work this paper cites.

Rich feature hierarchies for accurate object detection and semantic segmentation

Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014

Earlier work this paper cites.

Fully convolutional networks for semantic segmentation

Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015

Earlier work this paper cites.

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016

Cited alongside, same era.

Residual attention network for image classification

Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017

Cited alongside, same era.

Squeeze-and-excitation networks

Jie Hu, Li Shen, and Gang Sun · 2018

Cited alongside, same era.

Cbam: Convolutional block attention module

Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon · 2018

Cited alongside, same era.

Bam: Bottleneck attention module

Original

Jongchan Park, Sanghyun Woo, Joon-Young Lee, and In So Kweon · 2018

Cited alongside, same era.

Shufflenet: An extremely efficient convolutional neural network for mobile devices

Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018

Mobilenetv2: Inverted residuals and linear bottlenecks

Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018

Later among the works it cites.

Attention branch network: Learning of attention mechanism for visual explanation

Hiroshi Fukui, Tsubasa Hirakawa, Takayoshi Yamashita, and Hironobu Fujiyoshi · 2019

Later among the works it cites.

Scaling vision transformers

Original

Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2021

Closest in time.

A review on the attention mechanism of deep learning

Zhaoyang Niu, Guoqiang Zhong, and Hui Yu · 2021

Closest in time.

Rotate to attend: Convolutional triplet attention module

Diganta Misra, Trikay Nalamada, Ajay Uppili Arasanipalai, and Qibin Hou · 2021

Closest in time.