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Typologies of attentional networks
Nature Reviews Neuroscience, 2006Attention is a central theme in cognitive science - it exemplifies the links between the brain and behaviour, and binds psychology to the techniques of neuroscience. A visionary model suggested by Michael Posner described attention as a set of independent control networks. This challenged the previously held view of attention as a uniform concept.
Amir, Raz, Jason, Buhle
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2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019
Multi-band images beyond RGB are becoming popular in both commercial applications and research datasets, yet existing deep learning models were designed for academic RGB datasets. In this talk, we propose Channel Attention Networks (CAN), a deep learning model that uses soft attention on individual channels.
Alexei Bastidas, Hanlin Tang
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Multi-band images beyond RGB are becoming popular in both commercial applications and research datasets, yet existing deep learning models were designed for academic RGB datasets. In this talk, we propose Channel Attention Networks (CAN), a deep learning model that uses soft attention on individual channels.
Alexei Bastidas, Hanlin Tang
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Psychiatrische Praxis, 2004
In recent years it has been possible to treat attention as an organ system with its own anatomy, circuitry and set of functions. We view attention in terms of three interrelated neural networks in the human brain. These networks carry out the specific functions of developing and maintaining the alert state, orienting to sensory input, and executive ...
Jin, Fan, Michael, Posner
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In recent years it has been possible to treat attention as an organ system with its own anatomy, circuitry and set of functions. We view attention in terms of three interrelated neural networks in the human brain. These networks carry out the specific functions of developing and maintaining the alert state, orienting to sensory input, and executive ...
Jin, Fan, Michael, Posner
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Self Supervision for Attention Networks
2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021In recent years, the attention mechanism has become a fairly popular concept and has proven to be successful in many machine learning applications. However, deep learning models do not employ supervision for these attention mechanisms which can improve the model’s performance significantly.
Badri N. Patro +3 more
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Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
Attention is typically used to select informative sub-phrases that are used for prediction. This paper investigates the novel use of attention as a form of feature augmentation, i.e, casted attention. We propose Multi-Cast Attention Networks (MCAN), a new attention mechanism and general model architecture for a potpourri of ranking tasks in the ...
Yi Tay, Luu Anh Tuan, Siu Cheung Hui
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Attention is typically used to select informative sub-phrases that are used for prediction. This paper investigates the novel use of attention as a form of feature augmentation, i.e, casted attention. We propose Multi-Cast Attention Networks (MCAN), a new attention mechanism and general model architecture for a potpourri of ranking tasks in the ...
Yi Tay, Luu Anh Tuan, Siu Cheung Hui
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Graph Attention in Attention Network for Image Denoising
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023Bo Jiang 0017 +4 more
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Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition
Biomimetics, 2023Ge Xu, Wenzhong Lin
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Attention Spiking Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Yonghong Tian, Lei Deng, Guoqi Li
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Attention-based graph neural networks: a survey
Artificial Intelligence Review, 2023Fanrong Meng +2 more
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