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Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs).
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A feedforward architecture accounts for rapid categorization
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Recurrent models of visual attention
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Deep networks with internal selective attention through feedback connections
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Look and think twice: Capturing Top-Down visual attention with feedback convolutional neural networks
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SALICON: Saliency in context
M. Jiang, S. Huang, J. Duan, and Q. Zhao · 2015
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Eye tracking assisted extraction of attentionally important objects from videos
K. Shanmuga Vadivel, T. Ngo, M. Eckstein, and B. S. Manjunath · 2015
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Learning visual biases from human imagination
C. Vondrick, H. Pirsiavash, A. Oliva, and A. Torralba · 2015
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Image captioning with semantic attention
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
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M. Biparva and J. Tsotsos · 2017
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SCA-CNN: Spatial and Channel-Wise attention in convolutional networks for image captioning
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S. Bell, C. Lawrence Zitnick, K. Bala, and R. Girshick · 2016
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Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
R. M. Cichy, A. Khosla, D. Pantazis, A. Torralba, and A. Oliva · 2016
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Human attention in visual question answering: Do humans and deep networks look at the same regions?
A. Das, H. Agrawal, L. Zitnick, D. Parikh, and D. Batra · 2016
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Leveraging the wisdom of the crowd for Fine-Grained recognition
J. Deng, J. Krause, M. Stark, and L. Fei-Fei · 2016
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How deep is the feature analysis underlying rapid visual categorization?
S. Eberhardt, J. Cader, and T. Serre · 2016
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psiturk: An open-source framework for conducting replicable behavioral experiments online
T. M. Gureckis, J. Martin, J. McDonnell, A. S. Rich, D. Markant, A. Coenen, D. Halpern, J. B. Hamrick, and P. Chan · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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What are the visual features underlying human versus machine vision?
D. Linsley, S. Eberhardt, T. Sharma, P. Gupta, and T. Serre · 2017
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Dual attention networks for multimodal reasoning and matching
H. Nam, J. W. Ha, and J. Kim · 2017
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SmoothGrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
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Residual attention network for image classification
F. Wang, M. Jiang, C. Qian, S. Yang, C. Li, H. Zhang, X. Wang, and X. Tang · 2017
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Interpreting deep visual representations via network dissection
B. Zhou, D. Bau, A. Oliva, and A. Torralba · 2017
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Learn to pay attention
S. Jetley, N. A. Lord, N. Lee, and P. H. S. Torr · 2018
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Attentive systems: A survey
T. V. Nguyen, Q. Zhao, and S. Yan · 2018
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Bam: Bottleneck attention module
J. Park, S. Woo, J.-Y. Lee, and I.-S. Kweon · 2018
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Differential attention for visual question answering
B. Patro and V. P. Namboodiri · 2018
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