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We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN).
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Learning to combine foveal glimpses with a third-order boltzmann machine
H. Larochelle and G. E. Hinton · 2010
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Labelme: a database and web-based tool for image annotation
B. Russell, A. Torralba, K. Murphy, and W. T. Freeman · 2010
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Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
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Learning attentional policies for object tracking and recognition in video with deep networks
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Domain adaptation for object recognition: An unsupervised approach
R. Gopalan, R. Li, and R. Chellappa · 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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Transfer learning by borrowing examples for multiclass object detection
J. J. Lim, R. Salakhutdinov, and A. Torralba · 2011
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Searching for objects driven by context
B. Alexe, N. Heess, Y. W. Teh, and V. Ferrari · 2012
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Undoing the damage of dataset bias
A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba · 2012
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Learning to relate images
R. Memisevic · 2013
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Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
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Lsda: Large scale detection through adaptation
J. Hoffman, S. Guadarrama, E. S. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko · 2014
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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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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Recurrent models of visual attention
V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu · 2014
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Decoupled deep neural network for semi-supervised semantic segmentation
S. Hong, H. Noh, and B. Han · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Feedforward semantic segmentation with zoom-out features
M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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D. Pathak, E. Shelhamer, J. Long, and T. Darrell · 2014
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Learning to learn, from transfer learning to domain adaptation: A unifying perspective
N. Patricia and B. Caputo · 2014
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Learning generative models with visual attention
Y. Tang, N. Srivastava, and R. R. Salakhutdinov · 2014
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Learning categories from few examples with multi model knowledge transfer
T. Tommasi, F. Orabona, and B. Caputo · 2014
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Multiple object recognition with visual attention
J. Ba, V. Mnih, and K. Kavukcuoglu · 2015
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Semantic image segmentation with deep convolutional nets and fully connected CRFs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
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BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
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G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille · 2015
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Constrained convolutional neural networks for weakly supervised segmentation
D. Pathak, P. Krähenbühl, and T. Darrell · 2015
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Very deep convolutional networks for large-scale image recognition
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Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr · 2015
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