Fetching the paper…
Reading the bibliography…
This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos.
Interactive graph cuts for optimal boundary & region segmentation of objects in nd images
Y. Y. Boykov and M.-P. Jolly · 2001
Earlier work this paper cites.
Grabcut: Interactive foreground extraction using iterated graph cuts
C. Rother, V. Kolmogorov, and A. Blake · 2004
Earlier work this paper cites.
Random walks for image segmentation
L. Grady · 2006
Earlier work this paper cites.
Geodesic matting: A framework for fast interactive image and video segmentation and matting
X. Bai and G. Sapiro · 2009
Earlier work this paper cites.
Image segmentation with a bounding box prior
V. Lempitsky, P. Kohli, C. Rother, and T. Sharp · 2009
Earlier work this paper cites.
Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Earlier work this paper cites.
Crowdsourcing annotations for visual object detection
H. Su, J. Deng, and L. Fei-Fei · 2012
Earlier work this paper cites.
Grabcut in one cut
M. Tang, L. Gorelick, O. Veksler, and Y. Boykov · 2013
Earlier work this paper cites.
Semantic object selection
E. Ahmed, S. Cohen, and B. Price · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
Earlier work this paper cites.
MILcut: A sweeping line multiple instance learning paradigm for interactive image segmentation
J. Wu, Y. Zhao, J.-Y. Zhu, S. Luo, and Z. Tu · 2014
Earlier work this paper cites.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Earlier work this paper cites.
Fully convolutional multi-class multiple instance learning
D. Pathak, E. Shelhamer, J. Long, and T. Darrell · 2015
Cited alongside, same era.
Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollár · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Secrets of grabcut and kernel k-means
M. Tang, I. Ben Ayed, D. Marin, and Y. Boykov · 2015
Cited alongside, same era.
What’s the Point: Semantic Segmentation with Point Supervision
A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Closest in time.
Regional interactive image segmentation networks
J. Hao Liew, Y. Wei, W. Xiong, S.-H. Ong, and J. Feng · 2017
Closest in time.
Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Closest in time.
Webly supervised semantic segmentation
B. Jin, M. V. Ortiz Segovia, and S. Susstrunk · 2017
Closest in time.
Simple does it: Weakly supervised instance and semantic segmentation
A. Khoreva, R. Benenson, J. Hosang, M. Hein, and B. Schiele · 2017
Closest in time.
Video object segmentation with re-identification
X. Li, Y. Qi, Z. Wang, K. Chen, Z. Liu, J. Shi, P. Luo, C. C. Loy, and X. Tang · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
What makes for effective detection proposals?
J. Hosang, R. Benenson, P. Dollár, and B. Schiele · 2016
Cited alongside, same era.
Click carving: Segmenting objects in video with point clicks
S. D. Jain and K. Grauman · 2016
Cited alongside, same era.
Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
D. Lin, J. Dai, J. Jia, K. He, and J. Sun · 2016
Cited alongside, same era.
A benchmark dataset and evaluation methodology for video object segmentation
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung · 2016
Cited alongside, same era.
Learning to refine object segments
P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár · 2016
Cited alongside, same era.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Cited alongside, same era.
Convolutional oriented boundaries: From image segmentation to high-level tasks
K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. V. Gool · 2017
Closest in time.
Extreme clicking for efficient object annotation
D. P. Papadopoulos, J. R. Uijlings, F. Keller, and V. Ferrari · 2017
Closest in time.
Training object class detectors with click supervision
D. P. Papadopoulos, J. R. Uijlings, F. Keller, and V. Ferrari · 2017
Closest in time.
Multiscale combinatorial grouping for image segmentation and object proposal generation
J. Pont-Tuset, P. Arbelaez, J. T. Barron, F. Marques, and J. Malik · 2017
Closest in time.
The 2017 DAVIS challenge on video object segmentation
J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool · 2017
Closest in time.
Holistically-nested edge detection
S. Xie and Z. Tu · 2017
Closest in time.
Deep grabcut for object selection
N. Xu, B. Price, S. Cohen, J. Yang, and T. Huang · 2017
Closest in time.
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Closest in time.