A review on the attention mechanism of deep learning
Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …
[HTML][HTML] Deep learning attention mechanism in medical image analysis: Basics and beyonds
With the improvement of hardware computing power and the development of deep learning
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …
Feedback network for image super-resolution
Recent advances in image super-resolution (SR) explored the power of deep learning to
achieve a better reconstruction performance. However, the feedback mechanism, which …
achieve a better reconstruction performance. However, the feedback mechanism, which …
Attention, please! A survey of neural attention models in deep learning
A de Santana Correia, EL Colombini - Artificial Intelligence Review, 2022 - Springer
In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …
limited ability to process competing sources, attention mechanisms select, modulate, and …
Attention mechanism in neural networks: where it comes and where it goes
D Soydaner - Neural Computing and Applications, 2022 - Springer
A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …
inspired by the human visual system into neural networks was introduced. This idea is …
Deep convolutional neural networks for image classification: A comprehensive review
Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …
1980s. However, despite a few scattered applications, they were dormant until the mid …
Residual attention network for image classification
In this work, we propose" Residual Attention Network", a convolutional neural network using
attention mechanism which can incorporate with state-of-art feed forward network …
attention mechanism which can incorporate with state-of-art feed forward network …
[HTML][HTML] Attention in psychology, neuroscience, and machine learning
GW Lindsay - Frontiers in computational neuroscience, 2020 - frontiersin.org
Attention is the important ability to flexibly control limited computational resources. It has
been studied in conjunction with many other topics in neuroscience and psychology …
been studied in conjunction with many other topics in neuroscience and psychology …
Squeeze-and-excitation networks
Convolutional neural networks are built upon the convolution operation, which extracts
informative features by fusing spatial and channel-wise information together within local …
informative features by fusing spatial and channel-wise information together within local …
Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning
Visual attention has been successfully applied in structural prediction tasks such as visual
captioning and question answering. Existing visual attention models are generally spatial …
captioning and question answering. Existing visual attention models are generally spatial …