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Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes.
Use of watersheds in contour detection
S. Beucher and C. Lantuejoul · 1976
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The watershed transformation applied to image segmentation
S. Beucher · 1991
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
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Segmentation of 3d head mr images using morphological reconstruction under constraints and automatic selection of markers
P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár · 2001
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Improved watershed transform for medical image segmentation using prior information
V. Grau, A. Mewes, M. Alcañiz, R. Kikinis, and S. Warfield · 2004
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
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The watershed concept and its use in segmentation: a brief history
F. Meyer · 2012
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Multiscale Combinatorial Grouping
P. Arbelaez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik · 2014
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Simultaneous detection and segmentation
B. Hariharan, P. Arbelaez, R. Girshick, and J. Malik · 2014
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Instance segmentation of indoor scenes using a coverage loss
N. Silberman, D. Sontag, and R. Fergus · 2014
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Bottom-up instance segmentation using deep higher-order crfs
Y. Chen, X. Liu, and M. Yang · 2015
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End-to-end instance segmentation and counting with recurrent attention
X. Liang, Y. Wei, X. Shen, J. Yang, L. Lin, and S. Yan · 2015
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Fully Convolutional Networks for Semantic Segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Finely-grained annotated datasets for image-based plant phenotyping
M. Minervini, A. Fischbach, H.Scharr, , and S. Tsaftaris · 2015
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Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollár · 2015
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Iterative instance segmentation
K. Li, B. Hariharan, and J. Malik · 2016
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Reversible recursive instance-level object segmentation
X. Liang, Y. Wei, X. Shen, Z. Jie, J. Feng, L. Lin, and S. Yan · 2016
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Learning to refine object segments
P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár · 2016
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End-to-end instance segmentation and counting with recurrent attention
M. Ren and R. Zemel · 2016
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Recurrent Instance Segmentation
B. Romera-Paredes and P. H. S. Torr · 2016
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Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
J. Uhrig, M. Cordts, U. Franke, and T. Brox · 2016
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K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Monocular object instance segmentation and depth ordering with cnns
Z. Zhang, A. Schwing, S. Fidler, and R. Urtasun · 2015
Cited alongside, same era.
Bottom-up instance segmentation using deep higher-order crfs
A. Arnab and P. Torr · 2016
Cited alongside, same era.
The Cityscapes Dataset for Semantic Urban Scene Understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
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Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
C. F. G. Ghiasi · 2016
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Instance-level Segmentation of Vehicles by Deep Contours
J. van den Brand, M. Ochs, and R. Mester · 2016
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Multi-Scale Context Aggregation by Dilated Convolutions
F. Yu and V. Koltun · 2016
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A multipath network for object detection
S. Zagoruyko, A. Lerer, T.-Y. Lin, P. O. Pinheiro, S. Gross, S. Chintala, and P. Dollár · 2016
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Instance-Level Segmentation with Deep Densely Connected MRFs
Z. Zhang, S. Fidler, and R. Urtasun · 2016
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H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2016
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