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We propose a novel algorithm for weakly supervised semantic segmentation based on image-level class labels only.
Overview of the h.264/avc video coding standard
T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra · 2003
Earlier work this paper cites.
”grabcut”: Interactive foreground extraction using iterated graph cuts
C. Rother, V. Kolmogorov, and A. Blake · 2004
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.
Learning object class detectors from weakly annotated video
A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari · 2012
Earlier work this paper cites.
Neil: Extracting visual knowledge from web data
X. Chen, A. Shrivastava, and A. Gupta · 2013
Earlier work this paper cites.
Fast object segmentation in unconstrained video
A. Papazoglou and V. Ferrari · 2013
Earlier work this paper cites.
Discriminative segment annotation in weakly labeled video
K. Tang, R. Sukthankar, J. Yagnik, and L. Fei-Fei · 2013
Earlier work this paper cites.
Fast edge-preserving patchmatch for large displacement optical flow
L. Bao, Q. Yang, and H. Jin · 2014
Earlier work this paper cites.
Learning everything about anything: Webly-supervised visual concept learning
S. Divvala, A. Farhadi, and C. Guestrin · 2014
Earlier work this paper cites.
Supervoxel-consistent foreground propagation in video
S. D. Jain and K. Grauman · 2014
Earlier work this paper cites.
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
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.
Fully convolutional multi-class multiple instance learning
D. Pathak, E. Shelhamer, J. Long, and T. Darrell · 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
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Webly supervised learning of convolutional networks
X. Chen and A. Gupta · 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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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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Semantic image segmentation via deep parsing network
Z. Liu, X. Li, P. Luo, C. C. Loy, and X. Tang · 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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What’s the Point: Semantic Segmentation with Point Supervision
A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei · 2016
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Object detection, tracking, and motion segmentation for object-level video segmentation
B. Drayer and T. Brox · 2016
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Laplacian pyramid reconstruction and refinement for semantic segmentation
G. Ghiasi and C. C. Fowlkes · 2016
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Learning transferrable knowledge for semantic segmentation with deep convolutional neural network
S. Hong, J. Oh, H. Lee, and B. Han · 2016
Later among the works it cites.
Seed, expand and constrain: Three principles for weakly-supervised image segmentation
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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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Weakly-and semi-supervised learning of a DCNN for semantic image segmentation
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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From image-level to pixel-level labeling with convolutional networks
P. O. Pinheiro and R. Collobert · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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A. Kolesnikov and C. H. Lampert · 2016
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The unreasonable effectiveness of noisy data for fine-grained recognition
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and L. Fei-Fei · 2016
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Track and transfer: Watching videos to simulate strong human supervision for weakly-supervised object detection
K. Kumar Singh, F. Xiao, and Y. Jae Lee · 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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Efficient piecewise training of deep structured models for semantic segmentation
G. Lin, C. Shen, A. van dan Hengel, and I. Reid · 2016
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Hierarchically gated deep networks for semantic segmentation
G.-J. Qi · 2016
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Learning semantic segmentation with weakly-annotated videos
P. Tokmakov, K. Alahari, and C. Schmid · 2016
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Gaussian conditional random field network for semantic segmentation
R. Vemulapalli, O. Tuzel, M.-Y. Liu, and R. Chellapa · 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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