Fetching the paper…
Reading the bibliography…
We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries.
Scudder, H.J.: Probability of error of some adaptive pattern-recognition machines. IEEE T-IT 11(3) (1965)
1965
Earlier work this paper cites.
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS (2002)
2002
Earlier work this paper cites.
Shotton, J., Winn, J., Rother, C., Criminisi, A.: Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: ECCV (2006)
2006
Earlier work this paper cites.
Vasconcelos, M., Vasconcelos, N., Carneiro, G.: Weakly supervised top-down image segmentation. In: CVPR (2006)
2006
Earlier work this paper cites.
Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: ICCV (2007)
2007
Earlier work this paper cites.
Verbeek, J., Triggs, B.: Region classification with Markov field aspect models. In: CVPR (2007)
2007
Earlier work this paper cites.
Toyoda, T., Hasegawa, O.: Random field model for integration of local information and global information. IEEE T-PAMI 30(8) (2008)
2008
Earlier work this paper cites.
Verbeek, J., Triggs, W.: Scene segmentation with CRFs learned from partially labeled images. In: NIPS (2008)
2008
Earlier work this paper cites.
He, X., Zemel, R.S.: Learning hybrid models for image annotation with partially labeled data. In: NIPS (2009)
2009
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. IJCV 88(2) (2010)
2010
Earlier work this paper cites.
Nowozin, S., Gehler, P.V., Lampert, C.H.: On parameter learning in CRF-based approaches to object class image segmentation. In: ECCV (2010)
2010
Earlier work this paper cites.
Vezhnevets, A., Buhmann, J.M.: Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning. In: CVPR (2010)
2010
Earlier work this paper cites.
Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV (2011)
2011
Earlier work this paper cites.
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: NIPS (2011)
2011
Earlier work this paper cites.
Vezhnevets, A., Ferrari, V., Buhmann, J.M.: Weakly supervised semantic segmentation with a multi-image model. In: ICCV (2011)
2011
Earlier work this paper cites.
Carreira, J., Sminchisescu, C.: CPMC: Automatic object segmentation using constrained parametric min-cuts. IEEE T-PAMI 34(7) (2012)
2012
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)
2012
Earlier work this paper cites.
Liu, S., Yan, S., Zhang, T., Xu, C., Liu, J., Lu, H.: Weakly supervised graph propagation towards collective image parsing. IEEE T-MM 14(2) (2012)
2012
Earlier work this paper cites.
Vezhnevets, A., Ferrari, V., Buhmann, J.M.: Weakly supervised structured output learning for semantic segmentation. In: CVPR (2012)
2012
Cited alongside, same era.
Zhang, L., Song, M., Liu, Z., Liu, X., Bu, J., Chen, C.: Probabilistic graphlet cut: Exploiting spatial structure cue for weakly supervised image segmentation. In: CVPR (2013)
2013
Cited alongside, same era.
Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR (2014)
2014
Cited alongside, same era.
Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.H.S.: BING: Binarized normed gradients for objectness estimation at 300fps. In: CVPR (2014)
2014
Cited alongside, same era.
Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: CVPR (2015)
2015
Later among the works it cites.
Pourian, N., Karthikeyan, S., Manjunath, B.: Weakly supervised graph based semantic segmentation by learning communities of image-parts. In: CVPR (2015)
2015
Later among the works it cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. IJCV 115(3) (2015)
2015
Later among the works it cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. In: ICLR (2014)
2014
Cited alongside, same era.
Xie, W., Peng, Y., Xiao, J.: Weakly-supervised image parsing via constructing semantic graphs and hypergraphs. In: Multimedia (2014)
2014
Cited alongside, same era.
Xu, J., Schwing, A.G., Urtasun, R.: Tell me what you see and I will show you where it is. In: CVPR (2014)
2014
Cited alongside, same era.
Zhang, L., Gao, Y., Xia, Y., Lu, K., Shen, J., Ji, R.: Representative discovery of structure cues for weakly-supervised image segmentation. IEEE T-MM 16(2) (2014)
2014
Cited alongside, same era.
Zhang, L., Yang, Y., Gao, Y., Yu, Y., Wang, C., Li, X.: A probabilistic associative model for segmenting weakly supervised images. IEEE T-IP 23(9) (2014)
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: ICLR (2015)
2015
Cited alongside, same era.
2015
Later among the works it cites.
Xu, J., Schwing, A.G., Urtasun, R.: Learning to segment under various forms of weak supervision. In: CVPR (2015)
2015
Later among the works it cites.
Zhang, W., Zeng, S., Wang, D., Xue, X.: Weakly supervised semantic segmentation for social images. In: CVPR (2015)
2015
Later among the works it cites.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Object detectors emerge in deep scene CNNs. In: ICLR (2015)
2015
Later among the works it cites.
Bazzani, L., Bergamo, A., Anguelov, D., Torresani, L.: Self-taught object localization with deep networks. In: WACV (2016)
2016
Closest in time.
Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: What’s the point: Semantic segmentation with point supervision. ECCV (2016)
2016
Closest in time.
Hong, S., Oh, J., Lee, H., Han, B.: Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. CVPR (2016)
2016
Closest in time.
2016
Closest in time.
Kolesnikov, A., Lampert, C.H.: Improving weakly-supervised object localization by micro-annotation. BMVC (2016)
2016
Closest in time.
2016
Closest in time.
Krapac, J., Šegvic, S.: Weakly-supervised semantic segmentation by redistributing region scores to pixels. GCPR (2016)
2016
Closest in time.
Wei, Y., Liang, X., Chen, Y., Jie, Z., Xiao, Y., Zhao, Y., Yan, S.: Learning to segment with image-level annotations. Pattern Recognition (2016)
2016
Closest in time.
Zhou, B., Khosla, A., A., L., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)
2016
Closest in time.