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The selective visual attention mechanism in the human visual system (HVS) restricts the amount of information to reach visual awareness for perceiving natural scenes, allowing near real-time information processing with limited computational capacity [Koch and Ullman, 1987].
Temporal and spatial characteristics of selective encoding from visual displays
Charles W Eriksen and James E Hoffman · 1972
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Shifts in selective visual attention: Towards the underlying neural circuitry
Christof Koch and Shimon Ullman · 1987
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 1999
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 1999
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 1999
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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3d object representations for fine-grained categorization
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Learn to pay attention
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Progressive attention networks for visual attribute prediction
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Cbam: Convolutional block attention module
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Variational attention for sequence-to-sequence models
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Learn to pay attention
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High-frequency component helps explain the generalization of convolutional neural networks
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