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V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” Adv. Neural Inf. Process. Syst , 2011
2011
Earlier work this paper cites.
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik, “Semantic contours from inverse detectors,” in 2011 International Conference on Computer Vision . IEEE, 2011, pp. 991–998
2011
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 , 2014
Original
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014
Original
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 675–678
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.

Similar

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Hypercolumns for object segmentation and fine-grained localization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 447–456
2015
Cited alongside, same era.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 , 2015
Original
2015
Cited alongside, same era.
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille, “Attention to scale: Scale-aware semantic image segmentation,” arXiv preprint arXiv:1511.03339 , 2015
Original
2015
Cited alongside, same era.

Then

P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár, “Learning to refine object segments,” arXiv preprint arXiv:1603.08695 , 2016
Original
2016
Closest in time.
A. Bansal, X. Chen, B. Russell, A. Gupta, and D. Ramanan, “Pixelnet: Towards a general pixel-level architecture,” arXiv preprint arXiv:1609.06694 , 2016
Original
2016
Closest in time.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915 , 2016
Original
2016
Closest in time.
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis . Springer, 2016, pp. 179–187
2016
Closest in time.

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2016

Mixed context networks for semantic segmentation

Sun, Haiming, Xie, Di, Pu, Shiliang

Understand

Semantic segmentation is challenging as it requires both object-level information and pixel-level accuracy.

Recently, FCN-based systems gained great improvement in this area.
Unlike classification networks, combining features of different layers plays an important role in these dense prediction models, as these features contains information of different levels.
A number of models have been proposed to show how to use these features.

Built on

V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” Adv. Neural Inf. Process. Syst , 2011
2011
Earlier work this paper cites.
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik, “Semantic contours from inverse detectors,” in 2011 International Conference on Computer Vision . IEEE, 2011, pp. 991–998
2011
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 , 2014
Original
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014
Original
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 675–678
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.

Similar

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Hypercolumns for object segmentation and fine-grained localization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 447–456
2015
Cited alongside, same era.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 , 2015
Original
2015
Cited alongside, same era.
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille, “Attention to scale: Scale-aware semantic image segmentation,” arXiv preprint arXiv:1511.03339 , 2015
Original
2015
Cited alongside, same era.

Then

P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár, “Learning to refine object segments,” arXiv preprint arXiv:1603.08695 , 2016
Original
2016
Closest in time.
A. Bansal, X. Chen, B. Russell, A. Gupta, and D. Ramanan, “Pixelnet: Towards a general pixel-level architecture,” arXiv preprint arXiv:1609.06694 , 2016
Original
2016
Closest in time.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915 , 2016
Original
2016
Closest in time.
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis . Springer, 2016, pp. 179–187
2016
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr, “Conditional random fields as recurrent neural networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1529–1537

2015

Cited alongside, same era.

B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, “Semantic understanding of scenes through the ade20k dataset,” arXiv preprint arXiv:1608.05442 , 2016

Original

2016

Closest in time.