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We propose a new and, arguably, a very simple reduction of instance segmentation to semantic segmentation.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision , vol. 88, no. 2, pp. 303–338, Jun. 2010
2010
Earlier work this paper cites.
R. Rigamonti, A. Sironi, V. Lepetit, and P. Fua, “Learning separable filters,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 2754–2761
2013
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.
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Simultaneous detection and segmentation,” in European Conference on Computer Vision . Springer, 2014, pp. 297–312
2014
Earlier work this paper cites.
P. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik, “Multiscale combinatorial grouping,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 328–335
2014
Earlier work this paper cites.
H. Scharr, M. Minervini, A. Fischbach, and S. A. Tsaftaris, “Annotated image datasets of rosette plants,” in European Conference on Computer Vision. Zürich, Suisse , 2014, pp. 6–12
2014
Earlier work this paper cites.
J.-M. Pape and C. Klukas, “3-d histogram-based segmentation and leaf detection for rosette plants.” in ECCV Workshops (4) , 2014, pp. 61–74
2014
Earlier work this paper cites.
X. Yin, X. Liu, J. Chen, and D. M. Kramer, “Multi-leaf tracking from fluorescence plant videos,” in Image Processing (ICIP), 2014 IEEE International Conference on . IEEE, 2014, pp. 408–412
2014
Earlier work this paper cites.
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
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y.-T. Chen, X. Liu, and M.-H. Yang, “Multi-instance object segmentation with occlusion handling,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3470–3478
2015
Earlier work this paper cites.
J. Xu, A. G. Schwing, and R. Urtasun, “Learning to segment under various forms of weak supervision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2015, pp. 3781–3790
2015
Cited alongside, same era.
W. Zhang, S. Zeng, D. Wang, and X. Xue, “Weakly supervised semantic segmentation for social images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 2718–2726
2015
Cited alongside, same era.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/
2015
Cited alongside, same era.
M. V. Giuffrida, M. Minervini, and S. A. Tsaftaris, “Learning to count leaves in rosette plants,” 2016
2016
Later among the works it cites.
K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask r-cnn,” in The IEEE International Conference on Computer Vision (ICCV) , Oct 2017
2017
Later among the works it cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Later among the works it cites.
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei, “Fully convolutional instance-aware semantic segmentation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Later among the works it cites.
M. Ren and R. S. Zemel, “End-to-end instance segmentation with recurrent attention,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
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2016
Cited alongside, same era.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3213–3223
2016
Cited alongside, same era.
B. Romera-Paredes and P. H. S. Torr, “Recurrent instance segmentation,” in European Conference on Computer Vision . Springer, 2016, pp. 312–329
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
2016
Cited alongside, same era.
J. Dai, K. He, Y. Li, S. Ren, and J. Sun, “Instance-sensitive fully convolutional networks,” in European Conference on Computer Vision . Springer, 2016, pp. 534–549
2016
Cited alongside, same era.
J. Uhrig, M. Cordts, U. Franke, and T. Brox, “Pixel-level encoding and depth layering for instance-level semantic labeling,” in German Conference on Pattern Recognition . Springer, 2016, pp. 14–25
2016
Cited alongside, same era.
A. Kolesnikov and C. H. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” in Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV , 2016, pp. 695–711
2016
Cited alongside, same era.
M. Minervini, A. Fischbach, H. Scharr, and S. A. Tsaftaris, “Finely-grained annotated datasets for image-based plant phenotyping,” Pattern recognition letters , vol. 81, pp. 80–89, 2016
2016
Cited alongside, same era.
2017
Later among the works it cites.
M. Bai and R. Urtasun, “Deep watershed transform for instance segmentation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
Z. Hayder, X. He, and M. Salzmann, “Boundary-aware instance segmentation,” in Conference on Computer Vision and Pattern Recognition (CVPR) , no. EPFL-CONF-227439, 2017
2017
Later among the works it cites.
A. Arnab and P. H. S. Torr, “Pixelwise instance segmentation with a dynamically instantiated network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Later among the works it cites.
S. Liu, J. Jia, S. Fidler, and R. Urtasun, “Sgn: Sequential grouping networks for instance segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 3496–3504
2017
Later among the works it cites.
“PyTorch tensors and dynamic neural networks in python with strong gpu acceleration,” http://pytorch.org
2017
Later among the works it cites.
2018
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