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Attention in Attention Networks for Person Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021This paper generalizes the Attention in Attention (AiA) mechanism, in P. Fang et al., 2019 by employing explicit mapping in reproducing kernel Hilbert spaces to generate attention values of the input feature map. The AiA mechanism models the capacity of building inter-dependencies among the local and global features by the interaction of inner and ...
Pengfei Fang +5 more
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Anatomy of attentional networks [PDF]
The Anatomical Record Part B: the New Anatomist, 2004 AbstractAttention is a central theme in psychological science. As with other biological systems, attention has a distinct anatomy that carries out basic psychological functions. Disparate attentional networks correlate with discrete neural circuitry and can be influenced by specific brain injuries, states, and drugs.
Amir Raz
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Trends in Neurosciences, 1994
Recent brain-imaging and neurophysiological data indicate that attention is neither a property of a single brain area, nor of the entire brain. While attentional effects seem mediated by a relative amplification of blood flow and electrical activity in the cortical areas processing the attended computation, the details of how this is done through ...
M I, Posner, S, Dehaene
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Recent brain-imaging and neurophysiological data indicate that attention is neither a property of a single brain area, nor of the entire brain. While attentional effects seem mediated by a relative amplification of blood flow and electrical activity in the cortical areas processing the attended computation, the details of how this is done through ...
M I, Posner, S, Dehaene
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2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020
Recently, graph neural networks have achieved great success on the representation learning of the graph-structured data. However, these networks just consider the pairwise connection between nodes which cannot model the complicated connections of data in the real world. Thus, researchers began to pay attention to the hypergraph modeling.
Chaofan Chen +3 more
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Recently, graph neural networks have achieved great success on the representation learning of the graph-structured data. However, these networks just consider the pairwise connection between nodes which cannot model the complicated connections of data in the real world. Thus, researchers began to pay attention to the hypergraph modeling.
Chaofan Chen +3 more
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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
openaire +2 more sources