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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 architecture in an end-to-end training fashion.
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S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus · 2014
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V. Badrinarayanan, A. Handa, and R. Cipolla · 2015
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Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks
C. Cao, X. Liu, Y. Yang, Y. Yu, J. Wang, Z. Wang, Y. Huang, L. Wang, C. Huang, W. Xu, D. Ramanan, and T. S. Huang · 2015
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Attention to scale: Scale-aware semantic image segmentation
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille · 2015
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Convolutional feature masking for joint object and stuff segmentation
J. Dai, K. He, and J. Sun · 2015
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K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra · 2015
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K. He, X. Zhang, S. Ren, and J. Sun · 2015
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The application of two-level attention models in deep convolutional neural network for fine-grained image classification
T. Xiao, Y. Xu, K. Yang, J. Zhang, Y. Peng, and Z. Zhang · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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From facial parts responses to face detection: A deep learning approach
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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L. A. Hendricks, S. Venugopalan, M. Rohrbach, R. Mooney, K. Saenko, and T. Darrell · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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Multimodal residual learning for visual qa
J.-H. Kim, S.-W. Lee, D. Kwak, M.-O. Heo, J. Kim, J.-W. Ha, and B.-T. Zhang · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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Contextual priming and feedback for faster r-cnn
A. Shrivastava and A. Gupta · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, and V. Vanhoucke · 2016
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Diversified visual attention networks for fine-grained object classification
B. Zhao, X. Wu, J. Feng, Q. Peng, and S. Yan · 2016
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