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Contexts play an important role in the saliency detection task.
Learning representations by back-propagating errors
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Design and perceptual validation of performance measures for salient object segmentation
V. Movahedi and J. H. Elder · 2010
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Bottom-up saliency based on weighted sparse coding residual
B. Han, H. Zhu, and Y. Ding · 2011
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Center-surround divergence of feature statistics for salient object detection
D. A. Klein and S. Frintrop · 2011
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Efficient inference in fully connected crfs with gaussian edge potentials
P. Krähenbühl and V. Koltun · 2011
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Hybrid speech recognition with deep bidirectional lstm
A. Graves, N. Jaitly, and A.-r. Mohamed · 2013
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Hierarchical saliency detection
Q. Yan, L. Xu, J. Shi, and J. Jia · 2013
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Saliency detection via graph-based manifold ranking
C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang · 2013
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Saliency detection via graph-based manifold ranking
C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang · 2013
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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The secrets of salient object segmentation
Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille · 2014
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How to evaluate foreground maps?
R. Margolin, L. Zelnik-Manor, and A. Tal · 2014
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Recurrent models of visual attention
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Attention for fine-grained categorization
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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Global contrast based salient region detection
M.-M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.-M. Hu · 2015
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Saliency propagation from simple to difficult
C. Gong, D. Tao, W. Liu, S. J. Maybank, M. Fang, K. Fu, and J. Yang · 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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Visual saliency based on multiscale deep features
G. Li and Y. Yu · 2015
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Predicting eye fixations using convolutional neural networks
N. Liu, J. Han, D. Zhang, S. Wen, and T. Liu · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Renet: A recurrent neural network based alternative to convolutional networks
F. Visin, K. Kastner, K. Cho, M. Matteucci, A. Courville, and Y. Bengio · 2015
Ask, attend and answer: Exploring question-guided spatial attention for visual question answering
H. Xu and K. Saenko · 2016
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Stacked attention networks for image question answering
Z. Yang, X. He, J. Gao, L. Deng, and A. Smola · 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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Deep contrast learning for salient object detection
G. Li and Y. Yu · 2016
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Dhsnet: Deep hierarchical saliency network for salient object detection
N. Liu and J. Han · 2016
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Saliency detection with recurrent fully convolutional networks
L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan · 2016
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Cited alongside, same era.
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Saliency detection by multi-context deep learning
R. Zhao, W. Ouyang, H. Li, and X. Wang · 2015
Cited alongside, same era.
Visual saliency based on multiscale deep features
G. Li and Y. Yu · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
Cited alongside, same era.
Deeply supervised salient object detection with short connections
Q. Hou, M.-M. Cheng, X. Hu, A. Borji, Z. Tu, and P. Torr · 2017
Closest in time.
Deep level sets for salient object detection
P. Hu, B. Shuai, J. Liu, and G. Wang · 2017
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Attentive contexts for object detection
J. Li, Y. Wei, X. Liang, J. Dong, T. Xu, J. Feng, and S. Yan · 2017
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Non-local deep features for salient object detection
Z. Luo, A. Mishra, A. Achkar, J. Eichel, S. Li, and P.-M. Jodoin · 2017
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Learning to detect salient objects with image-level supervision
L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan · 2017
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A stagewise refinement model for detecting salient objects in images
T. Wang, A. Borji, L. Zhang, P. Zhang, and H. Lu · 2017
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Amulet: Aggregating multi-level convolutional features for salient object detection
P. Zhang, D. Wang, H. Lu, H. Wang, and X. Ruan · 2017
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Learning uncertain convolutional features for accurate saliency detection
P. Zhang, D. Wang, H. Lu, H. Wang, and B. Yin · 2017
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Deeply supervised salient object detection with short connections
Q. Hou, M.-M. Cheng, X. Hu, A. Borji, Z. Tu, and P. Torr · 2017
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Non-local deep features for salient object detection
Z. Luo, A. Mishra, A. Achkar, J. Eichel, S. Li, and P.-M. Jodoin · 2017
Closest in time.
Learning to detect salient objects with image-level supervision
L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan · 2017
Closest in time.
A stagewise refinement model for detecting salient objects in images
T. Wang, A. Borji, L. Zhang, P. Zhang, and H. Lu · 2017
Closest in time.
Amulet: Aggregating multi-level convolutional features for salient object detection
P. Zhang, D. Wang, H. Lu, H. Wang, and X. Ruan · 2017
Closest in time.
Learning uncertain convolutional features for accurate saliency detection
P. Zhang, D. Wang, H. Lu, H. Wang, and B. Yin · 2017
Closest in time.