-
1
-
-
84880793590
-
Opensurfaces: A richly annotated catalog of surface appearance
-
S. Bell, P. Upchurch, N. Snavely, and K. Bala. Opensurfaces: A richly annotated catalog of surface appearance. In SIGGRAPH, 2013.
-
(2013)
SIGGRAPH
-
-
Bell, S.1
Upchurch, P.2
Snavely, N.3
Bala, K.4
-
2
-
-
4344598245
-
An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision
-
Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. TPAMI, 2004.
-
(2004)
TPAMI
-
-
Boykov, Y.1
Kolmogorov, V.2
-
3
-
-
0033283778
-
Fast approximate energy minimization via graph cuts
-
Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. In ICCV, 1999.
-
(1999)
ICCV
-
-
Boykov, Y.1
Veksler, O.2
Zabih, R.3
-
4
-
-
0034844730
-
Interactive graph cuts for optimal boundary & region segmentation of objects in nd images
-
Y. Y. Boykov and M.-P. Jolly. Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In ICCV, 2001.
-
(2001)
ICCV
-
-
Boykov, Y.Y.1
Jolly, M.-P.2
-
5
-
-
85083954148
-
Semantic image segmentation with deep convolutional nets and fully connected crfs
-
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. In ICLR, 2015.
-
(2015)
ICLR
-
-
Chen, L.-C.1
Papandreou, G.2
Kokkinos, I.3
Murphy, K.4
Yuille, A.L.5
-
7
-
-
84973890848
-
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
-
J. Dai, K. He, and J. Sun. Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In ICCV, 2015.
-
(2015)
ICCV
-
-
Dai, J.1
He, K.2
Sun, J.3
-
8
-
-
84959216100
-
Convolutional feature masking for joint object and stuff segmentation
-
J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In CVPR, 2015.
-
(2015)
CVPR
-
-
Dai, J.1
He, K.2
Sun, J.3
-
9
-
-
77951298115
-
The pascal visual object classes (voc) challenge
-
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) Challenge. IJCV, 2010.
-
(2010)
IJCV
-
-
Everingham, M.1
Van Gool, L.2
Williams, C.K.3
Winn, J.4
Zisserman, A.5
-
11
-
-
33846207510
-
Random walks for image segmentation
-
L. Grady. Random walks for image segmentation. PAMI, 2006.
-
(2006)
PAMI
-
-
Grady, L.1
-
12
-
-
84856686500
-
Semantic contours from inverse detectors
-
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik. Semantic contours from inverse detectors. In ICCV, 2011.
-
(2011)
ICCV
-
-
Hariharan, B.1
Arbeláez, P.2
Bourdev, L.3
Maji, S.4
Malik, J.5
-
13
-
-
61349174704
-
Robust higher order potentials for enforcing label consistency
-
P. Kohli, P. H. Torr, et al. Robust higher order potentials for enforcing label consistency. IJCV, pages 302-324, 2009.
-
(2009)
IJCV
, pp. 302-324
-
-
Kohli, P.1
Torr, P.H.2
-
14
-
-
84897465786
-
Efficient inference in fully connected crfs with Gaussian edge potentials
-
P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with Gaussian edge potentials. In NIPS, 2012.
-
(2012)
NIPS
-
-
Krähenbühl, P.1
Koltun, V.2
-
15
-
-
0142192295
-
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
-
J. Lafferty, A. McCallum, and F. C. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
-
(2001)
ICML
-
-
Lafferty, J.1
McCallum, A.2
Pereira, F.C.3
-
16
-
-
33845571783
-
A closed form solution to natural image matting
-
A. Levin, D. Lischinski, and Y. Weiss. A closed form solution to natural image matting. CVPR, 2006.
-
(2006)
CVPR
-
-
Levin, A.1
Lischinski, D.2
Weiss, Y.3
-
18
-
-
85009931853
-
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. Microsoft COCO: Common objects in context. In ECCV. 2014.
-
(2014)
ECCV.
-
-
Lin, T.-Y.1
Maire, M.2
Belongie, S.3
Hays, J.4
Perona, P.5
Ramanan, D.6
Dollár, P.7
Zitnick, C.L.8
-
19
-
-
70349668761
-
Paint selection
-
ACM
-
J. Liu, J. Sun, and H.-Y. Shum. Paint selection. In SIGGRAPH, volume 28, page 69. ACM, 2009.
-
(2009)
SIGGRAPH
, vol.28
, pp. 69
-
-
Liu, J.1
Sun, J.2
Shum, H.-Y.3
-
20
-
-
84973860883
-
Semantic image segmentation via deep parsing network
-
Z. Liu, X. Li, P. Luo, C. C. Loy, and X. Tang. Semantic image segmentation via deep parsing network. In ICCV, 2015.
-
(2015)
ICCV
-
-
Liu, Z.1
Li, X.2
Luo, P.3
Loy, C.C.4
Tang, X.5
-
21
-
-
84959205572
-
Fully convolutional networks for semantic segmentation
-
J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
-
(2015)
CVPR
-
-
Long, J.1
Shelhamer, E.2
Darrell, T.3
-
22
-
-
84911444024
-
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. The role of context for object detection and semantic segmentation in the wild. In CVPR. 2014.
-
(2014)
CVPR.
-
-
Mottaghi, R.1
Chen, X.2
Liu, X.3
Cho, N.-G.4
Lee, S.-W.5
Fidler, S.6
Urtasun, R.7
Yuille, A.8
-
23
-
-
84973879016
-
Learning deconvolution network for semantic segmentation
-
H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic segmentation. In ICCV, 2015.
-
(2015)
ICCV
-
-
Noh, H.1
Hong, S.2
Han, B.3
-
24
-
-
84965124068
-
Weakly-and semi-supervised learning of a dcnn for semantic image segmentation
-
G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille. Weakly-and semi-supervised learning of a dcnn for semantic image segmentation. In ICCV, 2015.
-
(2015)
ICCV
-
-
Papandreou, G.1
Chen, L.-C.2
Murphy, K.3
Yuille, A.L.4
-
28
-
-
85009885014
-
-
arXiv:1409.0575
-
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. arXiv:1409.0575, 2014.
-
(2014)
Imagenet Large Scale Visual Recognition Challenge
-
-
Russakovsky, O.1
Deng, J.2
Su, H.3
Krause, J.4
Satheesh, S.5
Ma, S.6
Huang, Z.7
Karpathy, A.8
Khosla, A.9
Bernstein, M.10
-
29
-
-
33845423382
-
Textonboost: Joint appearance, shape and context modeling for mulit-class object recognition and segmentation
-
J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost: Joint appearance, shape and context modeling for mulit-class object recognition and segmentation. In ECCV, 2006.
-
(2006)
ECCV
-
-
Shotton, J.1
Winn, J.2
Rother, C.3
Criminisi, A.4
-
30
-
-
85083953063
-
Very deep convolutional networks for large-scale image recognition
-
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
-
(2015)
ICLR
-
-
Simonyan, K.1
Zisserman, A.2
-
31
-
-
84973861983
-
Conditional random fields as recurrent neural networks
-
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr. Conditional random fields as recurrent neural networks. In ICCV, 2015.
-
(2015)
ICCV
-
-
Zheng, S.1
Jayasumana, S.2
Romera-Paredes, B.3
Vineet, V.4
Su, Z.5
Du, D.6
Huang, C.7
Torr, P.8
|