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We present an approach to learn a dense pixel-wise labeling from image-level tags.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
Constrained differential optimization for neural networks
J. C. Platt and A. H. Barr · 1988
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
Solving linear programming problems with neural networks: a comparative study
S. H. Zak, V. Upatising, and S. Hui · 1994
Earlier work this paper cites.
Solving the multiple instance problem with axis-parallel rectangles
T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez · 1997
Earlier work this paper cites.
Support vector machines for multiple-instance learning
S. Andrews, I. Tsochantaridis, and T. Hofmann · 2002
Earlier work this paper cites.
Convex Optimization
S. Boyd and L. Vandenberghe · 2004
Earlier work this paper cites.
Multiple instance boosting for object detection
C. Zhang, J. C. Platt, and P. A. Viola · 2005
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
Earlier work this paper cites.
Posterior regularization for structured latent variable models
K. Ganchev, J. Graça, J. Gillenwater, and B. Taskar · 2010
Earlier work this paper cites.
Generalized expectation criteria for semi-supervised learning with weakly labeled data
G. S. Mann and A. McCallum · 2010
Earlier work this paper cites.
Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning
A. Vezhnevets and J. M. Buhmann · 2010
Earlier work this paper cites.
Semantic contours from inverse detectors
B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik · 2011
Earlier work this paper cites.
Efficient inference in fully connected CRFs with gaussian edge potentials
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Weakly supervised structured output learning for semantic segmentation
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A tractable inference algorithm for diagnosing multiple diseases
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Representative discovery of structure cues for weakly-supervised image segmentation
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Simultaneous detection and segmentation
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