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This paper presents a novel approach for learning instance segmentation with image-level class labels as supervision.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
Learning to detect a salient object
T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum · 2007
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.
Efficient inference in fully connected crfs with gaussian edge potentials
P. Krähenbühl and V. Koltun · 2011
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Earlier work this paper cites.
Multiscale combinatorial grouping
P. Arbeláez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik · 2014
Earlier work this paper cites.
Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 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.
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.
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.
Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2015
Earlier work this paper cites.
Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
Earlier work this paper cites.
Is object localization for free? - weakly-supervised learning with convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2015
Earlier work this paper cites.
Weakly-and semi-supervised learning of a DCNN for semantic image segmentation
G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille · 2015
Earlier work this paper cites.
From image-level to pixel-level labeling with convolutional networks
P. O. Pinheiro and R. Collobert · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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What’s the Point: Semantic Segmentation with Point Supervision
A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei · 2016
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Instance-sensitive fully convolutional networks
J. Dai, K. He, Y. Li, S. Ren, and J. Sun · 2016
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Instance-aware semantic segmentation via multi-task network cascades
J. Dai, 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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Instancecut: From edges to instances with multicut
A. Kirillov, E. Levinkov, B. Andres, B. Savchynskyy, and C. Rother · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Later among the works it cites.
Exploiting saliency for object segmentation from image level labels
S. J. Oh, R. Benenson, A. Khoreva, Z. Akata, M. Fritz, and B. Schiele · 2017
Later among the works it cites.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Later among the works it cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Later among the works it cites.
Learning random-walk label propagation for weakly-supervised semantic segmentation
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Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov and C. H. Lampert · 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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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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Learning semantic segmentation with weakly-annotated videos
C. S. Pavel Tokmakov, Karteek Alahari · 2016
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Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
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Convolutional random walk networks for semantic image segmentation
G. Bertasius, L. Torresani, S. X. Yu, and J. Shi · 2017
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P. Vernaza and M. Chandraker · 2017
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Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Y. Wei, J. Feng, X. Liang, M.-M. Cheng, Y. Zhao, and S. Yan · 2017
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Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation
J. Ahn and S. Kwak · 2018
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Detectron
R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He · 2018
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Weakly-supervised semantic segmentation network with deep seeded region growing
Z. Huang, X. Wang, J. Wang, W. Liu, and J. Wang · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
A. Kendall, Y. Gal, and R. Cipolla · 2018
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Proposal-free network for instance-level semantic object segmentation
X. Liang, L. Lin, Y. Wei, X. Shen, J. Yang, and S. Yan · 2018
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Semi-convolutional operators for instance segmentation
D. Novotny, S. Albanie, D. Larlus, and A. Vedaldi · 2018
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Learning to segment via cut-and-paste
T. Remez, J. Huang, and M. Brown · 2018
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Revisiting dilated convolution: A simple approach for weakly- and semi-supervised semantic segmentation
Y. Wei, H. Xiao, H. Shi, Z. Jie, J. Feng, and T. S. Huang · 2018
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Group normalization
Y. Wu and K. He · 2018
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Weakly supervised instance segmentation using class peak response
Y. Zhou, Y. Zhu, Q. Ye, Q. Qiu, and J. Jiao · 2018
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