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We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task.
A wavelet tour of signal processing
S. Mallat · 1999
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The PASCAL Visual Object Classes (VOC) Challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
Earlier work this paper cites.
Segmentation as selective search for object recognition
K. E. A. van de Sande, J. R. R. Uijlings, T. Gevers, and A. W. M. Smeulders · 2011
Earlier work this paper cites.
Multiscale combinatorial grouping
P. Arbelaez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
Earlier work this paper cites.
Convolutional feature masking for joint object and stuff segmentation
J. Dai, K. He, and J. Sun · 2015
Earlier work this paper cites.
Fast R-CNN
R. Girshick · 2015
Earlier work this paper cites.
Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
Earlier work this paper cites.
Decoupled deep neural network for semi-supervised semantic segmentation
S. Hong, H. Noh, and B. Han · 2015
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Proposal-free network for instance-level object segmentation
X. Liang, Y. Wei, X. Shen, J. Yang, L. Lin, and S. Yan · 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
Cited alongside, same era.
Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollar · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
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R-fcn: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 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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Learning transferrable knowledge for semantic segmentation with deep convolutional neural network
S. Hong, J. Oh, B. Han, and H. Lee · 2016
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Iterative instance segmentation
K. Li, B. Hariharan, and J. Malik · 2016
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Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
D. Lin, J. Dai, J. Jia, K. He, and J. Sun · 2016
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Object detection networks on convolutional feature maps
S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun · 2015
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Fully connected deep structured networks
A. G. Schwing and R. Urtasun · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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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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Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform
L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille · 2016
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Efficient piecewise training of deep structured models for semantic segmentation
G. Lin, C. Shen, A. van den Hengel, and I. Reid · 2016
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Multi-scale patch aggregation (mpa) for simultaneous detection and segmentation
S. Liu, X. Qi, J. Shi, H. Zhang, and J. Jia · 2016
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed · 2016
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Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2016
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Learning to refine object segments
P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollar · 2016
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Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2016
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Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
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A multipath network for object detection
S. Zagoruyko, A. Lerer, T.-Y. Lin, P. O. Pinheiro, S. Gross, S. Chintala, and P. Dollár · 2016
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